Review Special Issues

A review of data mining methods in financial markets

  • Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.

    Citation: Haihua Liu, Shan Huang, Peng Wang, Zejun Li. A review of data mining methods in financial markets[J]. Data Science in Finance and Economics, 2021, 1(4): 362-392. doi: 10.3934/DSFE.2021020

    Related Papers:

    [1] Andriana E. Lazou, Panagiota-Kyriaki Revelou, Spiridoula Kougioumtzoglou, Irini F. Strati, Anastasia Kanellou, Anthimia Batrinou . Cultured meat: A survey of awareness among Greek consumers. AIMS Agriculture and Food, 2024, 9(1): 356-373. doi: 10.3934/agrfood.2024021
    [2] Birabrata Nayak, Bibhu Prasad Panda . Modelling and optimization of texture profile of fermented soybean using response surface methodology. AIMS Agriculture and Food, 2016, 1(4): 409-418. doi: 10.3934/agrfood.2016.4.409
    [3] Artyom Lamanov, Yurij Ivanov, Rishat Iskhakov, Liliya Zubairova, Khamit Tagirov, Azat Salikhov . Beef quality indicators and their dependence on keeping technology of bull calves of different genotypes. AIMS Agriculture and Food, 2020, 5(1): 20-29. doi: 10.3934/agrfood.2020.1.20
    [4] Rinat R. Gadiev, Danis D. Khaziev, Chulpan R. Galina, Albert R. Farrakhov, Kamil D. Farhutdinov, Irina Yu. Dolmatova, Marina A. Kazanina, Gulnara F. Latypova . The use of chlorella in goose breeding. AIMS Agriculture and Food, 2019, 4(2): 349-361. doi: 10.3934/agrfood.2019.2.349
    [5] Sangam Dahal, Basanta Kumar Rai, Anish Dangal, Kishor Rai, Prekshya Timsina, Ramesh Koirala, Sanjay Chaudhary, Pankaj Dahal, Tanka Bhattarai, Angelo Maria Giuffrè . Evaluation of storage stability of refrigerated buffalo meat coated with hydrothermally treated potato starch incorporated with thyme (Thymus vulgaris) and ginger (Zingiber officinale) essential oil. AIMS Agriculture and Food, 2024, 9(4): 1110-1133. doi: 10.3934/agrfood.2024058
    [6] Guizhen Wang, Limin Hua, Victor R. Squires, Guozhen Du . What road should the grazing industry take on pastoral land in China?. AIMS Agriculture and Food, 2017, 2(4): 354-369. doi: 10.3934/agrfood.2017.4.354
    [7] Teti Estiasih, Jatmiko Eko Witoyo, Khofifah Putri Wulandari, Fadhillah Dwi Juniati, Widiastuti Setyaningsih, Hanifah Nuryani Lioe, Miguel Palma, Kgs Ahmadi, Hamidie Ronald Daniel Ray, Elya Mufidah . Stability comparison of conventional and foam-mat red and purple dried roselle calyces powder as a function of pH. AIMS Agriculture and Food, 2025, 10(1): 177-198. doi: 10.3934/agrfood.2025010
    [8] Enrica Bargiacchi, Margherita Campo, Annalisa Romani, Gilberto Milli, Sergio Miele . Hydrolysable Tannins from Sweet Chestnut (Castanea sativa Mill.) to improve Tobacco and Food/Feed Quality. Note 1: Fraction characterization, and Tobacco biostimulant effect for gall-nematode resistance. AIMS Agriculture and Food, 2017, 2(3): 324-338. doi: 10.3934/agrfood.2017.3.324
    [9] Svetlana N. Kovalchuk, Anna L. Arkhipova, Eugene A. Klimov . Development of real-time PCR assay for genotyping SNP rs41255693 in cattle SCD gene. AIMS Agriculture and Food, 2020, 5(1): 14-19. doi: 10.3934/agrfood.2020.1.14
    [10] Ariana Macieira, Helena Albano, Miguel Pinto, Raquel Linheiro, Joana Barbosa, Paula Teixeira . Evaluation of a bacteriocinogenic Lactobacillus plantarum strain on the microbiological characteristics of “Alheira de Vitela”. AIMS Agriculture and Food, 2019, 4(2): 223-236. doi: 10.3934/agrfood.2019.2.223
  • Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.



    Animal-derived foods, particularly meat and meat products, have a diverse array of nutrient compositions, characterized by significant contents of high-quality proteins, essential amino acids, B-group vitamins, minerals, and various other nutrients [1,2], making them important constituents of human nutrition worldwide. However, muscle foods (proteins of animal origin) are perishable products that are subject to several microbial contaminations (growth of pathogens and several microorganisms), resulting in their deterioration and quality loss [3,4,5]. In fact, meat quality deterioration can be driven by multiple mechanisms, namely microbial proliferation and lipid and protein oxidation, which all together impact the different properties of meat and meat products such color, flavor, texture, and nutritional value [4,6,7,8,9,10,11]. Besides the internal factors impacting the rate and extent of meat quality deterioration, numerous external factors such as oxygen, temperature, light exposition, preservative compounds, and processing techniques are key in the stability and final quality preservation [4,8]. Lipid and protein oxidation are the major non-microbial issues causing meat deterioration [4]. For example, oxidative reactions occurring during the manufacturing, distribution and storage of meat and meat products induce multiple physicochemical transformations and/or alterations, which consequently generate undesirable aromas that have detrimental impacts on the final quality, leading to consumer dissatisfaction and economic losses [12]. Several strategies are used to preserve and improve the stability, shelf-life, and quality of meat and meat products, namely, cold chain logistics, heat treatments, packaging innovations, and chemical preservatives [2]. However, the increasing concerns on food safety and the potential risks associated with the application of chemical and synthetic preservative agents/molecules [13,14], have resulted in a notable shift towards natural preservatives that seemed to have interesting properties to maintain or improve the stability of muscle foods. Overall, these natural molecules aim to inhibit or delay microbial growth, oxidative reactions, and enzymatic degradation, thereby extending the shelf-life of meat products and ensuring their safer consumption, resulting in consumer satisfaction [15]. For this purpose, numerous bio-preservatives have been explored, among which certain studies demonstrated antioxidant and antimicrobial effects of biosurfactants to preserve and extend the shelf-life of meat and meat-based products (Table 1). We aimed to review the applications of biosurfactants, a class of microorganism-formed compounds, in animal production and meat research, with specific emphasis on their antimicrobial and antioxidant activities, as compounds enabling extended shelf-life of meat products.

    Table 1.  Biosurfactants as antimicrobial agents against pathogens commonly tested in food industry with emphasis on meat and meat products.
    Source (origin) Biosurfactant tested Targeted microorganisms Refs.
    Pseudomonas fragi NMC25 Undefined Psychrophilic bacteria:
    Acinetobacter
    Shewanella
    Serratia
    [46]
    Bacillus subtilis Lipopeptides: surfactin, fengycin, mycosubtilin and their mixtures Paecilomyces variotti
    Byssochlamys fulv
    Candida krusei
    [43]
    Aneurinibacillus aneurinilyticus Lipopeptides Pseudomonas aeruginosa
    Escherichia coli
    Aspergillus brasiliensis
    Candida albicans
    [47,48,49]
    Commercial Rhamnolipid Rhamnolipid Bacillus cereus [50]
    S. bombicola Sophorolipid Staphylococcus aureus
    Listeria monocytogenes
    Salmonella enterica
    Escherichia coli
    [51]
    Rhodococcus fascians Trehalose lipid (glycolipid) Candida albican
    Escherichia coli
    [52]
    Pediococcus pentosaceus Lipoprotein Bacillus subtilis
    Pseudomonas aeruginosa
    Staphylococcus aureus
    Escherichia coli
    [53]
    Bacillus cereus Lipopeptide Aspergillus niger
    Penicillium fellutanum
    Cladosporium cladosporioides
    [54]
    Lactobacillus rhamnosus Glycolipid Bacillus subtilis
    Pseudomonas aeruginosa
    Staphylococcus aureus
    Escherichia coli
    [55]
    Lactobacillus paracasei subsp. tolerans N2 Glycolipoprotein Pseudomonas aeruginosa
    Pseudomonas putida
    Salmonella enteritidis
    Yersinia enterolitica
    Escherichia coli
    Bacillus sp.
    Staphylococcus aureus
    Proteus mirabilis
    Klebsiella pneumoniae
    [27,56]
    Commercial Rhamnolipid Rhamnolipid Escherichia coli
    Bacillus cereus
    [57]
    Pseudozyma aphidis DSM 70,725 Mannosylerythritol Lipids-A Listeria monocytogenes [57]
    Wickerhamomyces anomalus Glycolipid Escherichia coli
    Staphylococcus aureus
    Salmonella
    [58]
    Candida parapsilosis 13-Docosenamide Escherichia coli
    Staphylococcus aureus
    [59]

     | Show Table
    DownLoad: CSV

    Surface-active agents, commonly named surfactants, have versatile properties and occupy a significant position in the field of colloid and interfacial science due to their inherent amphiphilic nature. This characteristic facilitates the reduction of interfacial tension between disparate phases, including air-water, liquid-liquid (oil-water or water-oil), and liquid-solid interfaces [16]. For their production, two approaches can be distinguished. In the chemical approach, the organic chemistry serves as a founder of the covalent linkage of the amphiphilic (hydrophilic and hydrophobic) molecules, and ensures their structural integrity and functionality. In the biological approach, the biosurfactants may be produced by two primary pathways, either through direct extraction from plants or synthesized by an enzymatic or microbial process. It is worthy to mention that the scientific community refer to the term "biosurfactants" to the amphiphilic surface active agents that are obtained via fermentation process (microbial biosurfactants) [17]. Biosurfactants possess a hydrophilic moiety that can include carbohydrates, amino acids, cyclic peptides, phosphates, carboxylic acids, or alcohols, and a hydrophobic moiety that is mostly composed of long-chain fatty acids, hydroxyl fatty acids, or α-alkyl β-hydroxy fatty acids. This combination of hydrophilic and hydrophobic moieties provides to biosurfactants their amphiphilic property and contributes to their surfactant and antimicrobial activities [18].

    The production of biosurfactants depends on some factors that play a crucial role in the efficiency of the production yield, mostly the source of carbon and nitrogen, the carbon/nitrogen ratio, the content of salts and trace elements, and fermentation conditions [19]. The valorization of renewable wastes generated by food industry can be used to produce biosurfactants including bagasse, press mud, vegetables and fruits wastes, oil processing wastes, spent coffee ground, dairy products, fat, tallow, and lard [20,21]. Since the production of biosurfactants depends on carbon presence in the growth medium; lignocellulosic molecules (cellulose, lignin, hemicellulose) are selected for this purpose [20]. Accordingly, recent studies that used different renewable substrates for the production of microbial biosurfactants reported interesting yields (40.5 g/L) using Pseudomonas sp. Cultivated, for example, in canola waste frying oil [20,22]. Furthermore, rhamnolipid was produced by Burkholderia kururiensis KP23T isolated from an aquifer and glycolipoprotein by Lactobacillus paracasei subsp. tolerans N2 using sugar cane molasses as substrate [23]. However, the pathogenicity of strains and safety of produced biosurfactants should be assessed before any application.

    With the growing demand for surface-active agents to mitigate the environmental concerns associated with the use of chemical surfactants, microbial-derived surfactants are receiving increasing attention. In fact, they are regarded as eco-friendly alternatives because of their low toxicity, better biodegradability, high selectivity, and versatile activity/use under extreme conditions [19]. Moreover, microbial-derived surfactants, from both bacteria and yeast [24], present important advantage due to the ability of their production using agri-food wastes and/or renewable sources [21].

    Several studies on biosurfactants used lactic acid bacteria (LAB) for their production [25]. The use of LAB is mostly related to their GRAS status, their ability to promote human health, and strengthening the immune system. Thus, they have been proposed and evaluated for food preservation, food fermentation, improvement of the nutritional and sensory properties of food products. LAB have also interesting abilities to produce numerous functional metabolites that possess biopreservative potentials, such as enzymes, bacteriocins, biosurfactants, etc… [26]. The production of biosurfactants (glycolipoproteins biosurfactant) from LAB have been reported from several strains such as Lactobacillus paracasei subsp. tolerans N2 isolated from fermented cow milk [27]. Another study successfully isolated low-cost glycolipoprotein biosurfactant that is produced by Lactobacillus plantarum 60 FHE from cheese samples using food wastes [28]. Likewise, Kachrimanidou et al. [29] investigated the production of proteinaceous-based biosurfactants by several LAB strains, namely Lactobacillus rhamnosus, Lactobacillus casei, Lactobacillus pentosus, Lactobacillus coryniformis, Lactobacillus paracasei, and Lactobacillus plantarum using Cheese whey permeate as a low-cost fermentation feedstock for biosurfactant production. Our findings presented in this study indicate promising results for reducing the expenses related to biosurfactant production. Moreover, they support the advancement of refining food industry by-products to bolster the circularity and sustainability of food systems. Other types of biosurfactants were further produced by LAB strains: Cell-bound biosurfactants Phosphoglycoprotein (Lactobacillus rhamnosus CCM 1825), cell-bound lipoprotein biosurfactants (Lactobacillus pentosus CECT-4023T and cell-bound glycoprotein (Lactococcus lactis 53, Pediococcus acidilactici F70 [30]. Glycolipid and cell-bound glycolipid biosurfactants have been further produced by several other strains such as Weissella cibaria PN3 and Streptococcus thermophilus [30]. LAB and many other mesophilic species are positive amphiphilic agents' producers, likely Brukholederia kururiensis KP23 that produces rhamnolipid [27].

    Different recent studies evidenced the potential of extremophilic bacteria such as thermophilic microorganisms, to produce microbial surface tension agents [31]. For example, Bacillus subtilis, a thermophilic strain, was found as a good producer of surfactin that is a cyclic lipopeptide group of biosurfactants, possessing promoting properties such as high surface tension activity, high foaming capacity and stability, and antimicrobial activity [32]. Several other thermophilic bacteria strains are of interest: Bacillus licheniformis F2.2 [33], Bacillus safensis YKS2 [34] and Bacillus tequilensis [35].

    Biosurfactants compounds have also been produced from yeasts. For example, Candida bombicola produces acidic sophorolipid, acidic glucolipid and alcoholic glucoside [36]. The marine yeast Cyberlindnera saturnus SBPN-27 produces the glycolipid cybersan (trigalactomargarate) biosurfactant [37]. Furthermore, anionic biosurfactants with possible glycolipid structure were isolated from Geotrichum candidum, Galactomyces pseudocandidum and Candida tropicalis [38], glycolipid bio-emulsifiers were produced by high-salt-tolerant halophilic Cryptococcus sp. YLF [39], and a high-titer liamocin was produced by yeast-like fungus Aureobasidium pullulans [40]. Several fungal species have also been identified as promising sources, such as Mucor circinelloides UBOCC-A-109190, Mucor plumbeus UBOCC-A-111133, and Mucor mucedo UBOCC-A-101353 as glycolipid producers [41].

    Owing to their multiple functional characteristics, mostly emulsifying capacity, stabilization, and antimicrobial and antioxidant activities, biosurfactants are widely used in food industry, however, their utilization in meat research is very limited.

    The spoilage of meat products depends on the presence of various bacteria, mostly Salmonella spp., Campylobacter jejuni, Escherichia coli O157:H7, Listeria monocytogenes, Clostridium spp., Pseudomonas, Acinetobacter, Brochothrix thermosphacta, Lactobacillus spp., Enterobacter, molds, and yeasts. These spoilage microorganisms have the potential to cause outbreaks that can severely impact both public health and economy [2,42]. Therefore, effective preservation methods and strict quality control measures are crucial to mitigate meat spoilage and ensure food safety. For this purpose, some studies have focused on the use of biosurfactants as antimicrobial, fungicidal, fungistatic, and antibiofilm using different compounds produced from myriad microorganism [25,43,44,45]. Table 1 represents a few studies that evaluated the potential of biosurfactants as antimicrobial agents against microorganisms commonly present in meat and meat products. In Table 2 are summarized the available studies on the different applications of biosurfactants in meat research to preserve the quality or extend the shelf-life of meat and meat products.

    Table 2.  A non-exhaustive list of the various applications of biosurfactants in meat research.
    Biosurfactant(s) Sources Objective of the trials Type of meat matrix and procedure Main results Refs.
    Glycolipoprotein -Lactobacillus paracasei subsp. Tolerans N2
    -Lactobacillus casei subsp. casei TM1B
    In-situ effects of biosurfactants on the microbiological and physicochemical stabilities of raw ground goat meat stored at 4 ℃ Raw ground goat meat mixed with the glycolipid biosurfactant in sterile polyethylene bags • Decrease of the total aerobic counts, E. coli and Pseudomonas aeruginosa, leading to increased shelf-life (up to 15 days)
    • Better color stability
    • Inhibition of lipid oxidation
    • Inhibition of the production of basic volatile nitrogen
    [25,56]
    -Acetylated starch
    -Octenyl succinic
    -Anhydride starch
    -Ethyl (hydroxyethyl) cellulose
    -Dodecenyl succinylated inulin
    Commercial Formulation and development of a low-fat, printable, acceptable texture and fibrous sensation of plant-based meat analogue ink for potential utilization in 3D printing technology, focusing on exploring the biosurfactants and their functional properties Oil partially or entirely replaced with hydrophobically modified biosurfactants in a soy protein-based emulsion, in order to develop a reduced-fat meat analogue • Improvements of the pseudoplastic behavior with viscoelastic properties due to reduced-fat inks
    • Increase of consistency index recovery, frequency crossover point, and storage modulus
    [66,67]
    Undefined Bacillus subtilis DS03 Evaluate the biological activity of the biosurfactants against pathogenic strains and its potential sanitizer in open cleaning systems in the meat processing laboratory Designing an open cleaning system in the meat processing (after sausage production) where 3 products of the cleaning out-of-place system were formulated using biosurfactants • The cleaning of the equipment and utensils with biosurfactants led to a significant decrease of E. coli, S. aureus and L. monocytogenes [68]
    Sophorolipid Starmerella bombicola Develop a green packaging for the control of foodborne pathogens against poultry spoilage Formulated bioactive film incorporating polylactic acid and sophorolipid biosurfactant • Total inhibition of L. monocytogenes, S. aureus and reduction of 50% of Salmonella. spp bacterial population [69]
    Lipopeptide Enterobacter cloacae Investigate the antioxidant activity, emulsion stability and the conservation stability of raw beef patties using a biosurfactant Determination of the antioxidant activity of the lipopeptide biosurfactant • Significant oxidative stability of raw beef patties in the presence of the lipopeptide [70]
    Undefined Pseudomonas fragi Evaluate the effect of the biosurfactant on the spoilage ability and community dynamics of bacteria on the surface of chilled meat The biosurfactant was spread onto the surface of chicken breast. Several meat quality parameters have been evacuated including the microbiological counts, TVB-N, pH and meat color • Significant changes of the microbial diversity of the meat matrix, with a dominance of Pseudomonas in the population and effective reduction of spoilage state of meat [46]
    Lipopeptides Bacillus methylotrophicus Determine the antioxidant properties of the biosurfactant in raw beef patties during conservation; develop a novel type of beef patties coating: biosurfactant-gelatin-film for lipid oxidation prevention and shelf-life extension Ground beef meat used for patty formulations was treated with lipopeptide biosurfactant through two ways: patty formulation including the biosurfactant, and standard beef patty covered with the biosurfactant coating • Inhibition of lipid oxidation
    • Significant efficiency of the direct use of biosurfactant in lipid oxidation than coating
    [71]
    Sophorolipids Starmerella bombicola Develop chicken sausage with biosurfactant and exploring their antimicrobial, antioxidant and emulsifying properties. Formulation of the sausage prepared by mixing chicken ground meat, non-meat ingredients and additives, enriched with different concentrations of sophorolipid biosurfactants • Improvement of the sausage structural integrity: less porous mass, low cracks and better emulsion stability
    • Impact on color
    • Antimicrobial activity against C. perfringens and better antioxidant activity
    [72]
    Quillaja Saponin Commercial purchased Develop a natural and edible antioxidant agent via a formulation of a thymol nanoemulsion applied on raw chicken breast meat using biosurfactant. Formulation of a nanoemulsion using Quillaja Saponin biosurfactant in addition to other green solvents, where the meat samples were dipped during 14 days of storage in 4 ℃ • Effective green nanoemulsion
    • Antioxidant capability against lipid oxidation
    [73]

     | Show Table
    DownLoad: CSV

    The antimicrobial action of biosurfactants is widely explored for food safety purposes, which can act through several mechanisms: i) Modification of the surface charge, wettability and reduction of the interaction of bacterial population with the surface [60,61]; ii) interaction with intracellular constituents and perturbation of the normal functioning of microorganisms, thereby hindering crucial cellular processes, consequently, inhibiting the microbial growth, survival, and proliferation [61,62]; iii) induction at high concentration of necrosis and at low concentration fungal apoptosis [61,63]; and iv) binding to the phospholipid surface of the microbial cytoplasmic membrane through electrostatic forces, which can lead to the diffusion into the inner hydrophobic part of the membrane, consequently, weakening its lipid structure (Figure 1) and leaking its essential molecules [64].

    Figure 1.  Schematic antibacterial mechanism of surfactant micelles against E. coli. (Reprinted with permission from [65]).

    Biosurfactants were evidenced in several studies as a sustainable alternative to chemical compounds thanks to their antiadhesive and antibiofilm properties. Studies demonstrated that biosurfactants from two L. casei strains exhibited antibiofilm activities against S. aureus [74]. Other studies confirmed the biofilm inhibition of S. aureus by rhamnolipid produced by Enterobacter sp. UJS-RC [75] and others observed a reduction of 60% of Salmonella biofilm cells [76]. A study conducted by Mouafo and co-workers [56] reported that the glycolipoprotein biosurfactant from Lactobacillus paracasei was able to inhibit biofilm formation of six pathogen strains isolated from braised fish, namely Escherichia coli, Staphylococcus aureus, Salmonella enteritidis, Pseudomonas aeruginosa, Yersinia enterolitica, Proteus mirabilis, and Klebsiella pneumonia. It has been shown that biosurfactants are capable of modifying the physio-chemical properties of surfaces, subsequently reducing adhesion and biofilm formation [77].

    In order to mitigate the oxidation of proteins and lipids in muscle foods, natural compounds with antioxidant activity are widely used [1], from which biosurfactants are good candidates [25]. Some, as evidenced in Table 2, have demonstrated significant effects, such as the application of glycoprotein biosurfactant in inhibiting both lipid oxidation and the production of total volatile basic nitrogen in fresh ground goat meat [25]. Antioxidant activity of a lipopeptide biosurfactant was investigated by several mechanisms using several lipid oxidation tests, and it showed a significant impact on the inhibition of lipid oxidation in beef samples [70]. Furthermore, another earlier study confirmed the effective antioxidant activity of lipopeptides in ground beef patties up to 14 days, suggesting that the direct use of biosurfactants is more effective than using gelatin-based antioxidant packaging[71]. Kaiser et al. [72] carried out research to investigate the inhibition of lipid peroxidation of chicken sausages, and their findings revealed promising results using sophorolipid biosurfactant as a potential natural preservative. Quillaja Saponin biosurfactant also seemed to have a significant antioxidant activity on raw chicken breast meat through thymol nanoemulsion formulation [73]. Authors have explained the mechanisms behind this potency via ferric reducing antioxidant power of surfactin and rhamnolipid and further demonstrated that this might be due to hydroxyl groups in the lipopeptide molecular structure, hydrophobic amino acids (valine and leucine), acidic amino acids (aspartic acid and glutamic acid), and sulfur-containing amino acids such as methionine acids [78]. Moreover, biosurfactants exhibit a DPPH scavenging activity in which free radicals are neutralized by transferring either protons to electrons [78] or a hydrogen atom [77]. The results demonstrated the unsaturated lipids' ability to scavenge the reactive oxygen species and to prevent lipid peroxidation. Thus, this scavenging effect might be attributed to the presence of several active residues and to the hydrocarbon fatty acid chain in the peptide ring of the lipopeptide biosurfactant, which can react with the free radicals of DPPH. However, the lipid peroxidation inhibition could be attributed to the presence of both hydrophobic amino acids in the peptide ring and the acyl chain of beta-hydroxy fatty acids, thus improving the solubility of the peptide in the hydrophobic medium [77,78].

    Several meat products, such as sausages, frankfurters, bologna, and mortadella under the name of emulsion-type meat products, are produced [79]. These emulsion-type products are prepared by finely chopping meat from various sources (pork, beef, mutton, etc.), creating a stable mixture that effectively binds water and entraps fat. This unique emulsion imparts the textural properties to the product when cooked [80], which are considered as oil-in-water emulsions [81]. The quantity of fat plays a crucial role of the enhancement of flavor, texture, hardness, juiciness, mouth feel, moisture, and technological properties of these products such as pork backfat in sausages, meat batters, frankfurters, beef fat in beef burgers, and so on. However, the current concerns of consumers about various aspects of food quality and health is getting significant attention. Consequently, the reformulation of meat derivatives and especially emulsion based ones to enhance their health profile has become a vital strategy, particularly because consumers often perceive them as unhealthy because of the considerable quantity of fat that they contain [80]. Therefore, several studies have been performed to address the reduction of fat content, enhancement of fat profile, and transformation of liquid oil into a semi-solid system through various approaches such as hydrogenation, interesterification, oil bulking systems, and structured emulsions [82]. Accordingly, numerous emulsifying agents have been investigated, encompassing proteins, amphiphilic polysaccharides, protein-polysaccharide complexes, and low molecular weight surfactants. These agents play a crucial role in stabilizing emulsions, leading to improve product quality and functionality in various meat emulsion [83]. In the context of evaluating surfactants and among the very few studies, Serdaroğlu and co-workers [84] investigated the effect of partial beef fat replacement with gelled emulsion on functional and quality properties of model system meat emulsion supplemented with polyglyserol polyricinoleate surfactant. The findings indicated that partial replacement of beef fat with gelled emulsion could enhance cooking yield. Although the inclusion of gelled emulsion significantly influenced the textural properties of the samples, it did not have any adverse effects on water holding capacity and emulsion stability, concluding the potential benefits of using gelled emulsion in meat product formulations for the development of healthier meat products.

    The preparation of meat emulsion gels typically involves the production of a protein-stabilized emulsion, but it can be further supplemented with a hydrocolloid stabilizer or other ingredients, such as proteins, polysaccharides, and surfactants, after the formation of the emulsion [85]. This approach ensures the stability and functionality of the resulting emulsion gel [83,86]. For example, soybean protein serves as a source of surfactant molecules, effectively reducing the interfacial tension between oil and water in emulsion gels, which enhances the stability of the emulsion gels and contributes to their overall quality and functionality [83]. However, these surfactants are considered toxic agents causing environmental damage due to their chemical production process [87]. Therefore, as discussed earlier, biosurfactants gained recently a significant interest, including their use as emulsifying agents [88]. Even though many researchers investigated the use of biosurfactants as a green alternative of chemical surfactants in many fields globally and in the food industry, there is a lack of studies exploring biosurfactants in emulsion-based meat products. Thus, we believe that exploring the use of biosurfactants in fat meat replacers and emulsion-based meat formulations could yield noteworthy and valuable outcomes. Such investigations may open up new avenues for developing healthier and more sustainable meat products as well as satisfying the consumers' expectations.

    Green biopreservatives are receiving a huge interest worldwide due to their low toxicity and ecological purposes. In this sense, biosurfactants, which are amphiphilic molecules, are showing advantageous features that can be considered sustainable and biological alternatives to chemical and harmful surfactants, owing to their multiple and functional properties such has emulsification, stabilization, and bacterial and fungal inhibition, consequently enhancing food quality and extending the shelf-life of the products. These characteristics are widely demanded in the meat industry to reduce waste and prevent the perishability of meat products. In this article, we discussed the utilization of biosurfactants in food science with an emphasis on meat and meat products. We highlighted the multiple potential applications of biosurfactants as antimicrobial, antibiofilm, and/or antioxidant agents to tackle the problem of different types of meat products' spoilage and their stability. The effectiveness of the biosurfactants is not confirmed, but all the available data are promising, especially in extending the shelf-life of the products. Notwithstanding, they seemed to have interesting properties to be applied as stabilizers in meat-based emulsions. Further studies and evaluations of biosurfactants in meat research are needed to establish more evidence of their applications' potential and feasibility of use at larger scale.

    The authors declare that they have not used Artificial Intelligence (AI) tools in the creation of this article.

    All the authors declare that they have no conflicts of interest with the work presented here.



    [1] Abdalmageed W, Elosery A, Smith CE (2003) Non-parametric expectation maximization: a learning automata approach. In IEEE International Conference on Systems, 2003.
    [2] Agrawal L, Adane D (2021) Improved decision tree model for prediction in equity market using heterogeneous data. IETE J Res, 1–10.
    [3] Ahn JJ, Oh KJ, Kim TY, et al. (2011) Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Syst Appl 38: 2966–2973. doi: 10.1016/j.eswa.2010.08.085
    [4] Alberici A, Querci F (2015) The quality of disclosures on environmental policy: The profile of financial intermediaries. Corp Soc Resp Env Ma 23: 283–296. doi: 10.1002/csr.1375
    [5] Aljawazneh H, Mora AM, Garcia-Sanchez P, et al. (2021) Comparing the performance of deep learning methods to predict companies' financial failure. IEEE Access 9: 97010–97038.
    [6] Atsalakis GS, & Valavanis KP (2009) Surveying stock market forecasting techniques - part II: Soft computing methods. Expert Syst Appl 36: 5932–5941. doi: 10.1016/j.eswa.2008.07.006
    [7] Javed Awan M, Mohd Rahim MS, Nobanee H, et al. (2021) Social media and stock market prediction: A big data approach. Comput Mater Con 67: 2569–2583.
    [8] Barboza F, Kimura H, Altman E (2017) Machine learning models and bankruptcy prediction. Expert Syst Appl 83: 405–417. doi: 10.1016/j.eswa.2017.04.006
    [9] Bernardi M, Catania L (2018) Switching generalized autoregressive score copula models with application to systemic risk. J Appl Econometrics 34: 43–65. doi: 10.1002/jae.2650
    [10] Bielza C, Larranaga P (2014) Discrete bayesian network classifiers. ACM Comput Surv 47: 1–43.
    [11] Bishop CM (2006) Pattern Recognition and Machine Learning. Springer New York, 2006.
    [12] Borges TA, Neves RF (2020) Ensemble of machine learning algorithms for cryptocurrency investment with different data resampling methods. Appl Soft Comput 90: 106187. doi: 10.1016/j.asoc.2020.106187
    [13] Braun B (2018) Central banking and the infrastructural power of finance: the case of ECB support for repo and securitization markets. Socio-Econ Rev 18: 395–418.
    [14] Brusco MJ, Cradit JD (2001) A variable-selection heuristic for k-means clustering. Psychometrika 66: 249–270. doi: 10.1007/BF02294838
    [15] Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2: 121–167. doi: 10.1023/A:1009715923555
    [16] Bustos O, Pomares-Quimbaya A (2020) Stock market movement forecast: A systematic review. Expert Syst Appl 156: 113464. doi: 10.1016/j.eswa.2020.113464
    [17] Cagliero L, Garza P, Attanasio G, et al. (2020) Training ensembles of faceted classification models for quantitative stock trading. Computing 102: 1213–1225. doi: 10.1007/s00607-019-00776-7
    [18] Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE T Neural Networ 14: 1506–1518.
    [19] Carpinteiro OA, Leite JP, Pinheiro CA, et al. (2011) Forecasting models for prediction in time series. Artif Intell Rev 38: 163–171. doi: 10.1007/s10462-011-9275-1
    [20] Carta S, Ferreira A, Podda AS, et al. Multi-DQN: An ensemble of deep q-learning agents for stock market forecasting. Expert Syst Appl 164: 113820.
    [21] Cavalcante RC, Brasileiro RC, Souza VL, et al. Computational intelligence and financial markets: A survey and future directions. Expert Syst Appl 55: 194–211.
    [22] Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40: 200–210. doi: 10.1016/j.eswa.2012.07.021
    [23] Centanni S, Minozzo M (2006) Estimation and filtering by reversible jump MCMC for a doubly stochastic poisson model for ultra-high-frequency financial data. Stat Model 6: 97–118. doi: 10.1191/1471082X06st112oa
    [24] Chen AS, Leung MT, Pan S (2019) Financial hedging in energy market by cross-learning machines. Neural Comput Appl 32: 10321–10335. doi: 10.1007/s00521-019-04572-4
    [25] Chen HL, Liu DY, Yang B, et al. (2011) An adaptive fuzzy k-nearest neighbor method based on parallel particle swarm optimization for bankruptcy prediction. In Adv Knowl Discovery Data Min, 249–264. Springer Berlin Heidelberg, 2011.
    [26] Chen MY (2011) Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Syst Appl 38: 11261–11272. doi: 10.1016/j.eswa.2011.02.173
    [27] Chen S (2019) An effective going concern prediction model for the sustainability of enterprises and capital market development. Appl Econ 51: 3376–3388. doi: 10.1080/00036846.2019.1578855
    [28] Jin C, De-Lin L, Fen-Xiang M (2014) An improved ID3 decision tree algorithm. Adv Mater Res 962-965: 2842–2847. doi: 10.4028/www.scientific.net/AMR.962-965.2842
    [29] Chen Y, Hao Y (2017) A feature weighted support vector machine and k-nearest neighbor algorithm for stock market indices prediction. Expert Syst Appl 80: 340–355. doi: 10.1016/j.eswa.2017.02.044
    [30] Chen Z, Nazir A, Teoh EN, et al. Exploration of the effectiveness of expectation maximization algorithm for suspicious transaction detection in anti-money laundering. In 2014 IEEE Conference on Open Systems (ICOS). IEEE.
    [31] Cheng SH (2014) Predicting stock returns by decision tree combining neural network. Lect Notes Artif Int 8398: 352–360.
    [32] Cheng CH, Chan CP, Sheu YJ (2019) A novel purity-based k nearest neighbors imputation method and its application in financial distress prediction. Eng Appl Artif Intel 81: 283–299. doi: 10.1016/j.engappai.2019.03.003
    [33] Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20: 273–297.
    [34] Dai W (2021) Development and supervision of robo-advisors under digital financial inclusion in complex systems. Complexity 2021: 1–12.
    [35] Daugaard D Emerging new themes in environmental, social and governance investing: a systematic literature review. Account Financ 60: 1501–1530.
    [36] Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via theEMAlgorithm. J Royal Stat Soc 39: 1–22.
    [37] Deng S, Wang C, Wang M, et al. (2019) A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of china stock market. Appl Soft Comput 83: 105652. doi: 10.1016/j.asoc.2019.105652
    [38] Desokey EN, Badr A, Hegazy AF Enhancing stock prediction clustering using k-means with genetic algorithm. In 2017 13th International Computer Engineering Conference (ICENCO). IEEE.
    [39] Dong X, Yu Z, Cao W, et al. (2019) A survey on ensemble learning. Front Comput Sci 14: 241–258. doi: 10.1007/s11704-019-8208-z
    [40] Ekinci A, Erdal HI (2016) Forecasting bank failure: Base learners, ensembles and hybrid ensembles. Comput Econ 49: 677–686. doi: 10.1007/s10614-016-9623-y
    [41] Farid S, Tashfeen R, Mohsan T, et al. (2020) Forecasting stock prices using a data mining method: Evidence from emerging market. Int J Financ Econ.
    [42] Ferreira FGDC, Gandomi AH, Cardoso RTN (2020) Financial time-series analysis of brazilian stock market using machine learning. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE.
    [43] Ferreira LEB, Barddal JP, Gomes HM, et al. (2017) Improving credit risk prediction in online peer-to-peer (p2p) lending using imbalanced learning techniques. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE.
    [44] Fields D Constructing a new asset class: Property-led financial accumulation after the crisis. Econ Geogr 94: 118–140.
    [45] Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29: 131–163. doi: 10.1023/A:1007465528199
    [46] Gamage P (2016) New development: Leveraging 'big data' analytics in the public sector. Public Money Manage 36: 385–390. doi: 10.1080/09540962.2016.1194087
    [47] García S, Fernández A, Herrera F (2009) Enhancing the effectiveness and interpretability of decision tree and rule induction classifiers with evolutionary training set selection over imbalanced problems. Appl Soft Comput 9: 1304–1314. doi: 10.1016/j.asoc.2009.04.004
    [48] Garcia-Almanza AL, Tsang EP (2006) The repository method for chance discovery in financial forecasting, In International Conference on Knowledge-based Intelligent Information and Engineering Systems.
    [49] Gonzalez RT, Padilha CA, Barone DAC (2015) Ensemble system based on genetic algorithm for stock market forecasting. In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE.
    [50] Gou J, Ma H, Ou W, et al. (2019) A generalized mean distance-based k-nearest neighbor classifier. Expert Syst Appl 115: 356–372. doi: 10.1016/j.eswa.2018.08.021
    [51] Goyal K, Kumar S (2020) Financial literacy: A systematic review and bibliometric analysis. Int J Consum Stud 45: 80–105. doi: 10.1111/ijcs.12605
    [52] Guo S, He H, Huang X (2019) A multi-stage self-adaptive classifier ensemble model with application in credit scoring. IEEE Access 7: 78549–78559.
    [53] Han J, Pei J, Kamber M (2000) Data Mining: Concepts and Techniques.
    [54] Han J, Cheng H, Xin D, et al. (2007) Frequent pattern mining: current status and future directions. Data Min Knowl Discovery 15: 55–86. doi: 10.1007/s10618-006-0059-1
    [55] He H, Fan Y (2021) A novel hybrid ensemble model based on tree-based method and deep learning method for default prediction. Expert Syst Appl 176: 114899. doi: 10.1016/j.eswa.2021.114899
    [56] He S, Zheng J, Lin J, et al. (2020) Classification-based fraud detection for payment marketing and promotion. Comput Syst Sci Eng 35: 141–149. doi: 10.32604/csse.2020.35.141
    [57] Howe D, Costanzo M, Fey P, et al. (2008) The future of biocuration. Nature 455: 47–50. doi: 10.1038/455047a
    [58] Hssina B, Merbouha A, Ezzikouri H, et al. (2014) A comparative study of decision tree ID3 and c4.5. Int J Adv Comput Sci Appl 4.
    [59] Hsu YS, Lin SJ (2014) An emerging hybrid mechanism for information disclosure forecasting. Int J Mach Learn Cybern 7: 943–952. doi: 10.1007/s13042-014-0295-4
    [60] Huang C, Gao F, Jiang H (2014) Combination of biorthogonal wavelet hybrid kernel OCSVM with feature weighted approach based on EVA and GRA in financial distress prediction. Math Probl Eng 2014: 1–12.
    [61] Huang Q, Wang T, Tao D, et al. (2015) Biclustering learning of trading rules. IEEE T Cybern 45: 2287–2298.
    [62] Huang X, Tang H (2021) Measuring multi-volatility states of financial markets based on multifractal clustering model. J Forecast.
    [63] Iqbal R, Doctor F, More B, et al. (2020) Big data analytics: Computational intelligence techniques and application areas. Technol Forecast Soc 153: 119253. doi: 10.1016/j.techfore.2018.03.024
    [64] Jagadish HV, Gehrke J, Labrinidis A, et al. (2014) Big data and its technical challenges. Commun ACM 57: 86–94.
    [65] Rutkowski L, Jaworski M, Pietruczuk L, et al. (2014) The cart decision tree for mining data streams. Infor Sci.
    [66] Julia D, Pereira A, Silva RE (2018) Designing financial strategies based on artificial neural networks ensembles for stock markets. 1–8.
    [67] Kanhere P, Khanuja HK (2015) A methodology for outlier detection in audit logs for financial transactions. In 2015 International Conference on Computing Communication Control and Automation. IEEE.
    [68] Kercheval AN, Zhang Y (2015) Modelling high-frequency limit order book dynamics with support vector machines. Quant Financ 15: 1315–1329. doi: 10.1080/14697688.2015.1032546
    [69] Kewat P, Sharma R, Singh U, et al. (2017) Support vector machines through financial time series forecasting. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE.
    [70] Kilimci ZH (2019) Borsa tahmini için derin topluluk modellleri (DTM) ile finansal duygu analizi. Gazi niversitesi Mhendislik-Mimarlık Fakltesi Dergisi.
    [71] Kim SY, Upneja A (2021) Majority voting ensemble with a decision trees for business failure prediction during economic downturns. J Innovation Knowl 6: 112–123. doi: 10.1016/j.jik.2021.01.001
    [72] Kim YJ, Baik B, Cho S (2016) Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning. Expert Syst Appl 62: 32–43. doi: 10.1016/j.eswa.2016.06.016
    [73] Kirkos E, Spathis C, Manolopoulos Y (2007) Data mining techniques for the detection of fraudulent financial statements. Expert Syst Appl 32: 995–1003. doi: 10.1016/j.eswa.2006.02.016
    [74] Kotsiantis SB (2011) Decision trees: a recent overview. Artif Intell Rev 39: 261–283.
    [75] Kum HC, Ahalt S, Carsey TM (2011) Dealing with data: Governments records. Science 332: 1263–1263. doi: 10.1126/science.332.6035.1263-a
    [76] Kumar DA, Murugan S (2013) Performance analysis of indian stock market index using neural network time series model. In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering. IEEE.
    [77] Lee I (2017) Big data: Dimensions, evolution, impacts, and challenges. Bus Horizons 60: 293–303. doi: 10.1016/j.bushor.2017.01.004
    [78] Lee TK, Cho JH, Kwon DS, et al. (2019) Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Syst Appl 117: 228–242. doi: 10.1016/j.eswa.2018.09.005
    [79] Li H, Sun J, Sun BL (2009) Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors. Expert Syst Appl 36: 643–659. doi: 10.1016/j.eswa.2007.09.038
    [80] Li L, Wang J, Li X (2020) Efficiency analysis of machine learning intelligent investment based on k-means algorithm. IEEE Access 8: 147463–147470.
    [81] Li ST, Ho HF (2009) Predicting financial activity with evolutionary fuzzy case-based reasoning. Expert Syst Appl 36: 411–422. doi: 10.1016/j.eswa.2007.09.049
    [82] Li T, Li J, Liu Z, et al. (2018) Differentially private naive bayes learning over multiple data sources. Inf Sci 444: 89–104. doi: 10.1016/j.ins.2018.02.056
    [83] Li X, Wang F, Chen X (2015) Support vector machine ensemble based on choquet integral for financial distress prediction. Int J Pattern Recognit Artif Intell 29: 1550016. doi: 10.1142/S0218001415500160
    [84] Liang D, Tsai CF, Dai AJ, et al. (2017) A novel classifier ensemble approach for financial distress prediction. Knowl Inf Syst 54: 437–462. doi: 10.1007/s10115-017-1061-1
    [85] Liao SH, Chu PH, Hsiao PY (2012) Data mining techniques and applications - a decade review from 2000 to 2011. Expert Syst Appl 39: 11303–11311. doi: 10.1016/j.eswa.2012.02.063
    [86] Lin A, Shang P, Feng G, et al. (2012) APPLICATION OF EMPIRICAL MODE DECOMPOSITION COMBINED WITH k-NEAREST NEIGHBORS APPROACH IN FINANCIAL TIME SERIES FORECASTING. Fluct Noise Lett 11: 1250018. doi: 10.1142/S0219477512500186
    [87] Lin CS, Chiu SH, Lin TY (2012) Empirical mode decomposition-based least squares support vector regression for foreign exchange rate forecasting. Econ Model 29: 2583–2590. doi: 10.1016/j.econmod.2012.07.018
    [88] Lin G, Lin A, Cao J (2021) Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting. Expert Syst Appl 168: 114443. doi: 10.1016/j.eswa.2020.114443
    [89] Liu J, Lin CMM, Chao F (2019) Gradient boost with convolution neural network for stock forecast. In Adv Intell Syst Comput, 155–165.
    [90] Liu M, Luo K, Zhang J, et al. (2021) A stock selection algorithm hybridizing grey wolf optimizer and support vector regression. Expert Syst Appl 179: 115078. doi: 10.1016/j.eswa.2021.115078
    [91] Liu W, Zhao J, Wang D (2021) Data mining for energy systems: Review and prospect. WIREs Data Min Knowl Discovery 11.
    [92] Jan CL (2018) An effective financial statements fraud detection model for the sustainable development of financial markets: Evidence from taiwan. Sustainability 10: 513. doi: 10.3390/su10020513
    [93] Loukeris N, Eleftheriadis I, Livanis E (2013) A novel approach on hybrid support vector machines into optimal portfolio selection. In IEEE Int Symposium Signal Proc Inf TechnoL. IEEE.
    [94] Luintel KB, Khan M, Leon-Gonzalez R, et al. (2016) Financial development, structure and growth: New data, method and results. J Int Financ Mark Inst Money 43: 95–112. doi: 10.1016/j.intfin.2016.04.002
    [95] Luo B, Lin Z (2011) A decision tree model for herd behavior and empirical evidence from the online p2p lending market. Inf Syst e-Bus Manage 11: 141–160. doi: 10.1007/s10257-011-0182-4
    [96] Ma Y, Xu B, Xu X (2017) Real estate confidence index based on real estate news. Emerg Mark Financ Tr 54: 747–760. doi: 10.1080/1540496X.2016.1232193
    [97] Malliaris AG, Malliaris M (2015) What drives gold returns? a decision tree analysis. Financ Res Lett 13: 45–53. doi: 10.1016/j.frl.2015.03.004
    [98] Mazzarisi P, Barucca P, Lillo F, et al. (2020) A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market. Eur J Oper Res 281: 50–65. doi: 10.1016/j.ejor.2019.07.024
    [99] Mir-Juli M, Fiol-Roig G, Isern-Dey AP (2010) Decision trees in stock market analysis: Construction and validation. In Trends Applied Intelligent Systems-international Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, 2010.
    [100] Muja M, Lowe DG (2014) Scalable nearest neighbor algorithms for high dimensional data. IEEE T Pattern Anal 36: 2227–2240.
    [101] Naranjo R, Santos M (2019) A fuzzy decision system for money investment in stock markets based on fuzzy candlesticks pattern recognition. Expert Syst Appl 133: 34–48. doi: 10.1016/j.eswa.2019.05.012
    [102] Nardo M, Petracco‐Giudici M, Naltsidis, M (2015) WALKING DOWN WALL STREET WITH a TABLET: A SURVEY OF STOCK MARKET PREDICTIONS USING THE WEB. J Econ Surv 30: 356–369. doi: 10.1111/joes.12102
    [103] Al Nasseri A, Tucker A, de Cesare S (2015) Quantifying StockTwits semantic terms' trading behavior in financial markets: An effective application of decision tree algorithms. Expert Syst Appl 42: 9192–9210. doi: 10.1016/j.eswa.2015.08.008
    [104] Nassirtoussi AK, Aghabozorgi S, Wah TY, et al. (2014) Text mining for market prediction: A systematic review. Expert Syst Appl 41: 7653–7670. doi: 10.1016/j.eswa.2014.06.009
    [105] Nf J, Paolella MS, Polak P (2019) Heterogeneous tail generalized COMFORT modeling via cholesky decomposition. J Multivariate Anal 172: 84–106. doi: 10.1016/j.jmva.2019.02.004
    [106] Ng A, Jordan M (2002) On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In T. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems, volume 14. MIT Press, 2002. URL https://proceedings.neurips.cc/paper/2001/file/7b7a53e239400a13bd6be6c91c4f6c4e-Paper.pdf.
    [107] Ng KH, Khor KC (2016) StockProF: a stock profiling framework using data mining approaches. Inf Syst e-Bus Manage 15: 139–158.
    [108] Nie CX (2020) A network-based method for detecting critical events of correlation dynamics in financial markets. EPL (Europhys Lett) 131: 50001.
    [109] Ohana JJ, Ohana S, Benhamou E, et al. (2021) Explainable AI (XAI) models applied to the multi-agent environment of financial markets. In Explainable and Transparent AI and Multi-Agent Systems, pages 189–207. Springer International Publishing, 2021.
    [110] Olson DL (2006) Data mining in business services. Serv Bus 1: 181–193.
    [111] Oussous A, Benjelloun FZ, Lahcen AA, et al. (2018) Big data technologies: A survey. J King Saud University - Comput Inf Sci 30: 431–448.
    [112] Pan I, Bester D (2018) Fuzzy bayesian learning. IEEE T Fuzzy Syst 26: 1719–1731.
    [113] Paolella MS, Polak P, Walker PS (2019) Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns. J Econometrics 213: 493–515. doi: 10.1016/j.jeconom.2019.07.002
    [114] Patrizio A (2018) Idc: Expect 175 zettabytes of data worldwide by 2025. https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html.
    [115] Pei S, Shen T, Wang X, et al. (2020) 3dacn: 3d augmented convolutional network for time series data. Inf Sci 513: 17–29. doi: 10.1016/j.ins.2019.11.040
    [116] Peng Y, Wang G, Kou G, et al. (2011) An empirical study of classification algorithm evaluation for financial risk prediction. Appl Soft Comput 11: 2906–2915. doi: 10.1016/j.asoc.2010.11.028
    [117] Philip DJ, Sudarsanam N, Ravindran B (2018) Improved insights on financial health through partially constrained hidden markov model clustering on loan repayment data. ACM SIGMIS Database DATABASE Adv Inf Syst 49: 98–113.
    [118] Provost F, Fawcett T (2013) Data science and its relationship to big data and data-driven decision making. Big Data 1: 51–59. doi: 10.1089/big.2013.1508
    [119] Qian B, Rasheed K (2006) Stock market prediction with multiple classifiers. Appl Intell 26: 25–33. doi: 10.1007/s10489-006-0001-7
    [120] Quinlan JR (1986) Induction of decision trees. Mach Learn 1: 81–106.
    [121] Raudys Š (2000) How good are support vector machines? Neural Networks 13: 17–19.
    [122] Rokade A, Malhotra A, Wanchoo A (2016) Enhancing portfolio returns by identifying high growth companies in indian stock market using artificial intelligence. In 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE.
    [123] Rosati R, Romeo L, Goday CA (2020) Machine learning in capital markets: Decision support system for outcome analysis. IEEE Access 8: 109080–109091.
    [124] Roshan WDS, Gopura RARC, Jayasekara AGB, et al. (2016) Financial market forecasting by integrating wavelet transform and k-means clustering with support vector machine. In International Conference on Artificial Life and Robotics, 2016.
    [125] Roychowdhury S, Shroff N, Verdi RS (2019) The effects of financial reporting and disclosure on corporate investment: A review. J Account Econ 68: 101246. doi: 10.1016/j.jacceco.2019.101246
    [126] Rudin C, Daubechies I, Schapire RE, et al. (2004) The dynamics of adaboost: Cyclic behavior and convergence of margins. J Mach Learn Res 5: 1557–1595.
    [127] Ryans JP (2020) Textual classification of SEC comment letters. Rev Account Stud 26: 37–80.
    [128] Saidane M, Lavergne C (2009) Optimal prediction with conditionally heteroskedastic factor analysed hidden markov models. Comput Econ 34: 323–364. doi: 10.1007/s10614-009-9181-7
    [129] Salzberg SL (1994) C4.5: Programs for machine learning by j. ross quinlan. morgan kaufmann publishers, inc., 1993. Mach Learn 16: 235–240.
    [130] Samworth RJ (2012) Optimal weighted nearest neighbour classifiers. Annal Stat 40.
    [131] Schumaker RP, Chen H (2009) Textual analysis of stock market prediction using breaking financial news. ACM T Inf Syst 27: 1–19.
    [132] Seong N, Nam K (2021) Predicting stock movements based on financial news with segmentation. Expert Syst Appl 164: 113988. doi: 10.1016/j.eswa.2020.113988
    [133] Shamim S, Zeng J, Shariq SM, et al. (2019) Role of big data management in enhancing big data decision-making capability and quality among chinese firms: A dynamic capabilities view. Inform Manage 56: 103135. doi: 10.1016/j.im.2018.12.003
    [134] Shin HW, Sohn SY (2004) Segmentation of stock trading customers according to potential value. Expert Syst Appl 27: 27–33. doi: 10.1016/j.eswa.2003.12.002
    [135] Si YW, Yin J (2013) OBST-based segmentation approach to financial time series. Eng Appl Artif Intel 26: 2581–2596. doi: 10.1016/j.engappai.2013.08.015
    [136] Sinaga KP, Yang MS (2020) Unsupervised k-means clustering algorithm. IEEE Access 8: 80716–80727.
    [137] Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14: 199–222. doi: 10.1023/B:STCO.0000035301.49549.88
    [138] Soni S (2011) Applications of anns in stock market prediction: A survey. Int J Comput Sci Eng Technol 2: 71–83.
    [139] Sreedharan M, Khedr AM, El Bannany M (2020) A comparative analysis of machine learning classifiers and ensemble techniques in financial distress prediction. In 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 653–657.
    [140] Sun H, Rong W, Zhang J, et al. (2017) Stacked denoising autoencoder based stock market trend prediction via k-nearest neighbour data selection. In International Conference on Neural Information Processing. Springer, 882–892.
    [141] Sun J, Lang J, Fujita H, et al. (2018a) Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates. Inf Sci 425: 76–91. doi: 10.1016/j.ins.2017.10.017
    [142] Sun J, Li H, Fujita H, et al. (2020) Class-imbalanced dynamic financial distress prediction based on adaboost-SVM ensemble combined with SMOTE and time weighting. Inform Fusion 54: 128–144. doi: 10.1016/j.inffus.2019.07.006
    [143] Sun SL, Wei YJ, Wang SY (2018b) AdaBoost-LSTM ensemble learning for financial time series forecasting. In International Conference on Computational Science. Springer, 590–597.
    [144] Talebi H, Hoang W, Gavrilova ML (2014) Multi-scale foreign exchange rates ensemble for classification of trends in forex market. Proc Comput Sci 29: 2065–2075. doi: 10.1016/j.procs.2014.05.190
    [145] Tang L, Pan PH, Yao YY (2018a) EPAK: A computational intelligence model for 2-level prediction of stock indices. Int J Comput Commun 13: 268–279. doi: 10.15837/ijccc.2018.2.3187
    [146] Tang XB, Liu GC, Yang J, et al. (2018b) Knowledge-based financial statement fraud detection system: based on an ontology and a decision tree. Knowl Organ 45: 205–219. doi: 10.5771/0943-7444-2018-3-205
    [147] Tsai CF (2014) Combining cluster analysis with classifier ensembles to predict financial distress. Inform Fusion 16: 46–58. doi: 10.1016/j.inffus.2011.12.001
    [148] Tsai CF, Chiou YJ (2009) Earnings management prediction: A pilot study of combining neural networks and decision trees. Expert Syst Appl 36: 7183–7191. doi: 10.1016/j.eswa.2008.09.025
    [149] Vaghela VB, Vandra KH, Modi NK (2014) Mr-mnbc: Maxrel based feature selection for the multi-relational nave bayesian classifier. In Nirma University International Conference on Engineering, 1–9.
    [150] Wang B, Huang H, Wang X (2011a) A support vector machine based MSM model for financial short-term volatility forecasting. Neural Comput Appl 22: 21–28. doi: 10.1007/s00521-011-0742-z
    [151] Wang JZ, Wang JJ, Zhang ZG, et al. (2011b) Forecasting stock indices with back propagation neural network. Expert Syst Appl 38: 14346–14355.
    [152] Wang L, Zhu J (2008) Financial market forecasting using a two-step kernel learning method for the support vector regression. Ann Oper Res 174: 103–120. doi: 10.1007/s10479-008-0357-7
    [153] Wang Q, Xu W, Zheng H (2018) Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles. Neurocomputing 299: 51–61. doi: 10.1016/j.neucom.2018.02.095
    [154] Webb GI, Zheng Z (2004) Multistrategy ensemble learning: reducing error by combining ensemble learning techniques. IEEE T Knowl Data En 16: 980–991.
    [155] Weng B, Lu L, Wang X, et al. (2018) Predicting short-term stock prices using ensemble methods and online data sources. Expert Syst Appl 112: 258–273. doi: 10.1016/j.eswa.2018.06.016
    [156] Wu XD, Kumar V, Quinlan JR, et al. (2007) Top 10 algorithms in data mining. Knowl Inf Syst 14: 1–37.
    [157] Xing FZ, Cambria E, Welsch RE (2017) Natural language based financial forecasting: a survey. Artif Intell Rev 50: 49–73. doi: 10.1007/s10462-017-9588-9
    [158] Xu Y, Yang C, Peng S, et al. (2020) A hybrid two-stage financial stock forecasting algorithm based on clustering and ensemble learning. Appl Intell 50: 3852–3867. doi: 10.1007/s10489-020-01766-5
    [159] Yan L, Bai B (2016) Correlated industries mining for chinese financial news based on LDA trained with research reports. In 2016 16th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 131–135.
    [160] Yang R, Yu L, Zhao Y, et al. (2020) Big data analytics for financial market volatility forecast based on support vector machine. Int J Inf Manag 50: 452–462. doi: 10.1016/j.ijinfomgt.2019.05.027
    [161] Yeo B, Grant D (2018) Predicting service industry performance using decision tree analysis. Int J Inf Manag 38: 288–300. doi: 10.1016/j.ijinfomgt.2017.10.002
    [162] Yoo PD, Kim MH, Jan T (2005) Machine learning techniques and use of event information for stock market prediction: A survey and evaluation. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC06). IEEE. 2: 835–841.
    [163] Zhang Y, Yu G, Jin ZQ (2013) Violations detection of listed companies based on decision tree and k-nearest neighbor. In 2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings, 1671–1676.
    [164] Wu KP, Wu YP, Lee HM (2014) Stock trend prediction by using k-means and aprioriall algorithm for sequential chart pattern mining. J Inf Sci Eng 30: 653–667.
    [165] Zemke S (1999) Nonlinear index prediction. Physica A 269: 177–183.
    [166] Chenggang Zhang and Jingqing Jiang. A financial early warning algorithm based on ensemble learning. In 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA). IEEE, sep 2017. doi: 10.1109/ciapp.2017.8167192.
    [167] Zhang H, Li SF (2010) Forecasting volatility in financial markets. Key Eng Mater 439: 679–682. doi: 10.4028/www.scientific.net/KEM.439-440.679
    [168] Zhang JL, Härdle WK (2010) The bayesian additive classification tree applied to credit risk modelling. Comput Stat Data An 54: 1197–1205. doi: 10.1016/j.csda.2009.11.022
    [169] Zhang N, Lin A, Shang P (2017) Multidimensionalk-nearest neighbor model based on EEMD for financial time series forecasting. Physica A 477: 161–173. doi: 10.1016/j.physa.2017.02.072
    [170] Zhao QJ, SunQ, Che WG (2014) The application of bayesian discrimination in the analysis on media sector stock. Applied Mechanics and Materials 488: 1310–1313. doi: 10.4028/www.scientific.net/AMM.488-489.1310
    [171] Zhao Y (2021) Sports enterprise marketing and financial risk management based on decision tree and data mining. J Healthc Eng 2021: 1–8.
    [172] Guo ZQ, Wang HQ, Liu Q (2012) Financial time series forecasting using LPP and SVM optimized by PSO. Soft Comput 17: 805–818.
    [173] Zhu X, Che WG (2014) Research of outliers in time series of stock prices based on improved k-means clustering algorithm. Wit Trans Inf Commun Technol 46: 633–641.
    [174] Zhu Y, Xie C, Wang GJ, et al. (2016) Comparison of individual, ensemble and integrated ensemble machine learning methods to predict china's SME credit risk in supply chain finance. Neural Comput Appl 28: 41–50. doi: 10.1007/s00521-016-2304-x
    [175] Zhu Z, Liu N (2021) Early warning of financial risk based on k-means clustering algorithm. Complexity 2021: 1–12.
    [176] Zhuang Y, Xu Z, Tang Y (2015) A credit scoring model based on bayesian network and mutual information. In 2015 12th Web Information System and Application Conference (WISA).
    [177] Mirsadeghpour Zoghi SM, Saneie M, Tohidi G, et al. (2021) The effect of underlying distribution of asset returns on efficiency in dea models. Journal of Intelligent and Fuzzy Systems 40: 10273–10283. doi: 10.3233/JIFS-202332
    [178] Özorhan MO, Toroslu İH, Şehitoğlu OT (2018) Short-term trend prediction in financial time series data. Knowl Inf Syst 61: 397–429. doi: 10.1007/s10115-018-1303-x
  • This article has been cited by:

    1. V. V. Borshchov, N. Yе. Stadnytska, Study of the effectiveness of biosurfactant from the culture liquid of bacillus subtilis sp. in the extraction of biologically active substances of Thymus Serpyllum l., 2024, 7, 26177307, 100, 10.23939/ctas2024.02.100
    2. E. Khamis, D. E. Abd-El-Khalek, Manal Fawzy, Habiba M. Essam, A. M. Abdel-Gaber, J. M. Anwar, Enhancing the shelf life of natural scale inhibitors using bio preservatives, 2025, 15, 2045-2322, 10.1038/s41598-025-90831-5
    3. Afsana Habib Jui, Md Nur Hossain, Sadia Afrin, Banasree Bhowmik, Chaminda Senaka Ranadheera, Md. Habibur Rahman Bhuiyan, Microbial Biosurfactants: Prospect and Challenges for Application in Food Industry, 2025, 8755-9129, 1, 10.1080/87559129.2025.2478199
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(9156) PDF downloads(704) Cited by(11)

Figures and Tables

Figures(1)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog