Predicting bankruptcy risk with bidirectional long short-term memory and convolutional neural networks

Ghaneshvar Ramineni; Talayeh Razzaghi; Kash Barker; Ghaneshvar Ramineni; Talayeh Razzaghi; Kash Barker

doi:10.3934/jimo.2026142

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 8: 3999-4038. doi: 10.3934/jimo.2026142

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Research article

Predicting bankruptcy risk with bidirectional long short-term memory and convolutional neural networks

School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA

Received: 05 February 2026 Revised: 29 June 2026 Accepted: 10 July 2026 Published: 24 July 2026
Primary: 68T07; Secondary: 91G40, 62P20

Predicting the threat of bankruptcy risk and estimating company survival rate, especially under many challenges that might unfold, is of high economic importance. In this study, we aimed to predict the one-year-ahead and two-year-ahead bankruptcy status of companies using financial features (predictor variables) selected by the recursive feature elimination technique. We proposed the use of deep learning techniques, bidirectional long short-term memory (BiLSTM), and convolutional neural network (CNN) for bankruptcy prediction using temporal data. We compared prediction performance of models using the selected features with the original Altman Z-score financial features. To evaluate the proposed deep learning models, we used a sample of 4,884 companies (150 bankrupt and 4,734 non-bankrupt) drawn from the COMPUSTAT database (1994–2020) and compared them against six benchmark models, reweighted Altman Z-score, linear discriminant analysis, regularized logistic regression, random forest, XGBoost, and LightGBM, over 100 random 70/30 company-level splits, four feature sets, and two prediction horizons. The results showed that no single model dominates across all evaluation criteria: At the conventional decision threshold ($ t $ = 0.5), BiLSTM achieved the highest geometric mean of recall and specificity on the Altman feature set (0.653 and 0.615 for the one- and two-year-ahead horizons, respectively), whereas random forest attained the highest AUC on data-driven feature sets (up to 0.790). At that threshold, the tree ensembles flagged almost no failing companies because their calibrated probabilities rarely exceeded 0.5 under severe class imbalance; thus, the proposed deep models, and BiLSTM in particular, were the only ones that detected bankrupt companies reliably without tuning the classification threshold to the data. When the decision threshold was tuned near the bankruptcy base rate, tree-ensemble models recovered comparable balanced detection, underscoring the importance of evaluating bankruptcy prediction models using multiple performance criteria and operating thresholds rather than a single accuracy metric.
- bankruptcy prediction,
- financial distress,
- deep learning,
- bidirectional long short-term memory,
- convolutional neural network,
- class imbalance,
- Altman Z-score,
- feature selection,
- SHAP analysis
Citation: Ghaneshvar Ramineni, Talayeh Razzaghi, Kash Barker. Predicting bankruptcy risk with bidirectional long short-term memory and convolutional neural networks[J]. Journal of Industrial and Management Optimization, 2026, 22(8): 3999-4038. doi: 10.3934/jimo.2026142

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Abstract

Predicting the threat of bankruptcy risk and estimating company survival rate, especially under many challenges that might unfold, is of high economic importance. In this study, we aimed to predict the one-year-ahead and two-year-ahead bankruptcy status of companies using financial features (predictor variables) selected by the recursive feature elimination technique. We proposed the use of deep learning techniques, bidirectional long short-term memory (BiLSTM), and convolutional neural network (CNN) for bankruptcy prediction using temporal data. We compared prediction performance of models using the selected features with the original Altman Z-score financial features. To evaluate the proposed deep learning models, we used a sample of 4,884 companies (150 bankrupt and 4,734 non-bankrupt) drawn from the COMPUSTAT database (1994–2020) and compared them against six benchmark models, reweighted Altman Z-score, linear discriminant analysis, regularized logistic regression, random forest, XGBoost, and LightGBM, over 100 random 70/30 company-level splits, four feature sets, and two prediction horizons. The results showed that no single model dominates across all evaluation criteria: At the conventional decision threshold ($ t $ = 0.5), BiLSTM achieved the highest geometric mean of recall and specificity on the Altman feature set (0.653 and 0.615 for the one- and two-year-ahead horizons, respectively), whereas random forest attained the highest AUC on data-driven feature sets (up to 0.790). At that threshold, the tree ensembles flagged almost no failing companies because their calibrated probabilities rarely exceeded 0.5 under severe class imbalance; thus, the proposed deep models, and BiLSTM in particular, were the only ones that detected bankrupt companies reliably without tuning the classification threshold to the data. When the decision threshold was tuned near the bankruptcy base rate, tree-ensemble models recovered comparable balanced detection, underscoring the importance of evaluating bankruptcy prediction models using multiple performance criteria and operating thresholds rather than a single accuracy metric.

References

[1]	T. Telford, S. Mufson, PG & E, the nation's biggest utility company, files for bankruptcy after California wildfires, Washington Post, 2019.
[2]	J. H. Bliss, Financial and Operating Ratios in Management, New York: The Ronald Press, 1923.
[3]	A. C. Littleton, The 2 to 1 ratio analyzed, Certified Public Accountant, (1926), 244–246.
[4]	P. J. Fitzpatrick, A Comparison of the Ratios of Successful Industrial Enterprises with Those of Failed Companies, 1932.
[5]	A. H. Winakor, R. F. Smith, Changes in the financial structure of unsuccessful industrial corporations, Univ. Ill. Bull., 51 (1935).
[6]	C. Mervin, Financing Small Corporations: In Five Manufacturing Industries, 1926–36, National Bureau of Economic Research, 1942.
[7]	W. H. Beaver, Financial ratios as predictors of failure, J. Account. Res., 4 (1966), 71–111. https://doi.org/10.2307/24901717 doi: 10.2307/24901717
[8]	E. Altman, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, J. Finance, 23 (1968), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x doi: 10.1111/j.1540-6261.1968.tb00843.x
[9]	E. I. Altman, R. G. Haldeman, P. K. Narayanan, ZETA analysis: A new model to identify bankruptcy risk of corporations, J. Banking Finance, 1 (1977), 29–54. https://doi.org/10.1016/0378-4266(77)90017-6 doi: 10.1016/0378-4266(77)90017-6
[10]	E. I. Altman, M. Iwanicz-Drozdowska, E. K. Laitinen, A. Suvas, Financial distress prediction in an international context: A review and empirical analysis of Altman's Z-score model, J. Int. Financ. Manage. Account., 28 (2017), 131–171. https://doi.org/10.1111/jifm.12053 doi: 10.1111/jifm.12053
[11]	F. Barboza, H. Kimura, E. Altman, Machine learning models and bankruptcy prediction, Expert Syst. Appl., 83 (2017), 405–417. https://doi.org/10.1016/j.eswa.2017.04.006 doi: 10.1016/j.eswa.2017.04.006
[12]	R. C. Carton, C. W. Hofer, Measuring Organizational Performance: Metrics for Entrepreneurship and Strategic Management Research, Edward Elgar, 2006.
[13]	L. Cleofas-Sánchez, V. García, A. I. Marqués, J. S. Sánchez, Financial distress prediction using the hybrid associative memory with translation, Appl. Soft Comput., 44 (2016), 144–152. https://doi.org/10.1016/j.asoc.2016.04.005 doi: 10.1016/j.asoc.2016.04.005
[14]	J. Heo, J. Y. Yang, AdaBoost based bankruptcy forecasting of Korean construction companies, Appl. Soft Comput., 24 (2014), 494–499. https://doi.org/10.1016/j.asoc.2014.08.009 doi: 10.1016/j.asoc.2014.08.009
[15]	F. J. López Iturriaga, I. P. Sanz, Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks, Expert Syst. Appl., 42 (2015), 2857–2869. https://doi.org/10.1016/j.eswa.2014.11.025 doi: 10.1016/j.eswa.2014.11.025
[16]	C. F. Tsai, Y. F. Hsu, D. C. Yen, A comparative study of classifier ensembles for bankruptcy prediction, Appl. Soft Comput., 24 (2014), 977–984. https://doi.org/10.1016/j.asoc.2014.08.047 doi: 10.1016/j.asoc.2014.08.047
[17]	G. P. Naidu, K. Govinda, Bankruptcy prediction using neural networks, Proc. 2nd Int. Conf. Inventive Syst. Control (ICISC), (2018), 248–251. https://doi.org/10.1109/ICISC.2018.8399072 doi: 10.1109/ICISC.2018.8399072
[18]	D. K. Chandra, V. Ravi, I. Bose, Failure prediction of dotcom companies using hybrid intelligent techniques, Expert Syst. Appl., 36 (2009), 4830–4837. https://doi.org/10.1016/j.eswa.2008.05.047 doi: 10.1016/j.eswa.2008.05.047
[19]	F. Antunes, B. Ribeiro, F. Pereira, Probabilistic modeling and visualization for bankruptcy prediction, Appl. Soft Comput., 60 (2017), 831–843. https://doi.org/10.1016/j.asoc.2017.06.043 doi: 10.1016/j.asoc.2017.06.043
[20]	H. Kim, H. Cho, D. Ryu, Corporate bankruptcy prediction using machine learning methodologies with a focus on sequential data, Comput. Econ., 59 (2022), 1231–1249. https://doi.org/10.1007/s10614-021-10126-5 doi: 10.1007/s10614-021-10126-5
[21]	S. Smiti, M. Soui, Bankruptcy prediction using deep learning approach based on Borderline SMOTE, Inf. Syst. Front., 22 (2020), 1067–1083. https://doi.org/10.1007/s10796-020-10031-6 doi: 10.1007/s10796-020-10031-6
[22]	S. Ben Jabeur, N. Stef, P. Carmona, Bankruptcy prediction using the XGBoost algorithm and variable importance feature engineering, Comput. Econ., 61 (2023), 715–741. https://doi.org/10.1007/s10614-021-10227-1 doi: 10.1007/s10614-021-10227-1
[23]	A. Booth, E. Gerding, F. McGroarty, Automated trading with performance weighted random forests and seasonality, Expert Syst. Appl., 41 (2014), 3651–3661. https://doi.org/10.1016/j.eswa.2013.12.009 doi: 10.1016/j.eswa.2013.12.009
[24]	D. Liang, C. C. Lu, C. F. Tsai, G. A. Shih, Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study, Eur. J. Oper. Res., 252 (2016), 561–572. https://doi.org/10.1016/j.ejor.2016.01.012 doi: 10.1016/j.ejor.2016.01.012
[25]	G. Wang, J. Ma, S. Yang, An improved boosting based on feature selection for corporate bankruptcy prediction, Expert Syst. Appl., 41 (2014), 2353–2361. https://doi.org/10.1016/j.eswa.2013.09.033 doi: 10.1016/j.eswa.2013.09.033
[26]	Y. C. Hu, A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems, Soft Comput., 24 (2020), 4259–4268. https://doi.org/10.1007/s00500-019-04191-0 doi: 10.1007/s00500-019-04191-0
[27]	S. Y. Kim, A. Upneja, Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models, Econ. Model., 36 (2014), 354–362. https://doi.org/10.1016/j.econmod.2013.10.005 doi: 10.1016/j.econmod.2013.10.005
[28]	J. H. Min, Y. C. Lee, Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert Syst. Appl., 28 (2005), 603–614. https://doi.org/10.1016/j.eswa.2004.12.008 doi: 10.1016/j.eswa.2004.12.008
[29]	J. Uthayakumar, N. Metawa, K. Shankar, S. K. Lakshmanaprabu, Financial crisis prediction model using ant colony optimization, Int. J. Inf. Manage., 50 (2020), 538–556. https://doi.org/10.1016/j.ijinfomgt.2018.12.001 doi: 10.1016/j.ijinfomgt.2018.12.001
[30]	C. H. Wang, J. Z. Chen, Combining hidden Markov models with probabilistic Bayes networks to conduct business forecasting and risk simulation, Soft Comput., 25 (2021), 8773–8784. https://doi.org/10.1007/s00500-021-05784-4 doi: 10.1007/s00500-021-05784-4
[31]	C. Lohmann, S. Möllenhoff, T. Ohliger, Nonlinear relationships in bankruptcy prediction and their effect on the profitability of bankruptcy prediction models, J. Bus. Econ., 93 (2023), 1661–1690. https://doi.org/10.1007/s11573-022-01130-8 doi: 10.1007/s11573-022-01130-8
[32]	Q. Yu, Y. Miche, E. Séverin, A. Lendasse, Bankruptcy prediction using Extreme Learning Machine and financial expertise, Neurocomputing, 128 (2014), 296–302. https://doi.org/10.1016/j.neucom.2013.01.063 doi: 10.1016/j.neucom.2013.01.063
[33]	J. L. Bellovary, D. E. Giacomino, M. D. Akers, A review of bankruptcy prediction studies: 1930 to present, J. Financ. Educ., 33 (2007), 1–42.
[34]	Y. Wu, C. Gaunt, S. Gray, A comparison of alternative bankruptcy prediction models, J. Contemp. Account. Econ., 6 (2010), 34–45. https://doi.org/10.1016/j.jcae.2010.04.002 doi: 10.1016/j.jcae.2010.04.002
[35]	E. I. Altman, M. Balzano, A. Giannozzi, S. Srhoj, The Omega score: An improved tool for SME default predictions, J. Int. Counc. Small Bus., 4 (2023), 362–373. https://doi.org/10.1080/26437015.2023.2186284 doi: 10.1080/26437015.2023.2186284
[36]	J. M. Liberti, M. A. Petersen, Information: Hard and soft, Rev. Corp. Finance Stud., 8 (2019), 1–41. https://doi.org/10.1093/rcfs/cfy009 doi: 10.1093/rcfs/cfy009
[37]	R. Gao, S. Cui, Y. Wang, W. Xu, Predicting financial distress in high-dimensional imbalanced datasets: A multi-heterogeneous self-paced ensemble learning framework, Financ. Innov., 11 (2025), 50. https://doi.org/10.1186/s40854-024-00745-w doi: 10.1186/s40854-024-00745-w
[38]	L. Zhu, Z. Zhang, M. J. C. Crabbe, Exploring small-scale optimization coupling learning approaches for enterprises' financial health forecasts, Financ. Innov., 11 (2025), 78. https://doi.org/10.1186/s40854-024-00748-7 doi: 10.1186/s40854-024-00748-7
[39]	B. Branch, The costs of bankruptcy: A review, Int. Rev. Financ. Anal., 11 (2002), 39–57. https://doi.org/10.1016/S1057-5219(01)00068-0 doi: 10.1016/S1057-5219(01)00068-0
[40]	A. Bris, I. Welch, N. Zhu, The costs of bankruptcy: Chapter 7 liquidation versus Chapter 11 reorganization, J. Finance, 61 (2006), 1253–1303. https://doi.org/10.1111/j.1540-6261.2006.00872.x doi: 10.1111/j.1540-6261.2006.00872.x
[41]	F. Mai, S. Tian, C. Lee, L. Ma, Deep learning models for bankruptcy prediction using textual disclosures, Eur. J. Oper. Res., 274 (2019), 743–758. https://doi.org/10.1016/j.ejor.2018.10.024 doi: 10.1016/j.ejor.2018.10.024
[42]	J. Gamboa, Deep learning for time-series analysis, arXiv preprint, arXiv: 1701.01887, 2017.
[43]	A. Graves, J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Netw., 18 (2005), 602–610. https://doi.org/10.1016/j.neunet.2005.06.042 doi: 10.1016/j.neunet.2005.06.042
[44]	S. Siami-Namini, N. Tavakoli, A. Siami Namin, A comparison of ARIMA and LSTM in forecasting time series, Proc. 17th IEEE Int. Conf. Mach. Learn. Appl. (ICMLA), (2018), 1394–1401.
[45]	S. Lawrence, C. L. Giles, A. C. Tsoi, A. D. Back, Face recognition: A convolutional neural-network approach, IEEE Trans. Neural Netw., 8 (1997), 98–113. https://doi.org/10.1109/72.554195 doi: 10.1109/72.554195
[46]	A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, Commun. ACM, 60 (2017), 84–90. https://doi.org/10.1145/3065386 doi: 10.1145/3065386
[47]	S. Ghosh, D. L. Reilly, Credit card fraud detection with a neural-network, Proc. Hawaii Int. Conf. Syst. Sci., 3 (1994), 621–630. https://doi.org/10.1109/HICSS.1994.323314 doi: 10.1109/HICSS.1994.323314
[48]	N. Kalchbrenner, E. Grefenstette, P. Blunsom, A convolutional neural network for modelling sentences, arXiv preprint, arXiv: 1404.2188, 2014.
[49]	J. B. Yang, M. N. Nguyen, P. P. San, X. L. Li, S. Krishnaswamy, Deep convolutional neural networks on multichannel time series for human activity recognition, Proc. IJCAI, (2015), 3995–4001.
[50]	H. H. Nguyen, J. L. Viviani, S. Ben Jabeur, Bankruptcy prediction using machine learning and Shapley additive explanations, Rev. Quant. Finance Account., 65 (2025), 107–148. https://doi.org/10.1007/s11156-023-01192-x doi: 10.1007/s11156-023-01192-x
[51]	T. Hosaka, Bankruptcy prediction using imaged financial ratios and convolutional neural networks, Expert Syst. Appl., 117 (2019), 287–299. https://doi.org/10.1016/j.eswa.2018.09.039 doi: 10.1016/j.eswa.2018.09.039
[52]	M. J. Kim, D. K. Kang, Ensemble with neural networks for bankruptcy prediction, Expert Syst. Appl., 37 (2010), 3373–3379. https://doi.org/10.1016/j.eswa.2009.10.012 doi: 10.1016/j.eswa.2009.10.012
[53]	M. D. Odom, R. Sharda, A neural network model for bankruptcy prediction, Proc. IJCNN Int. Joint Conf. Neural Netw., (1990), 163–168.
[54]	Z. R. Yang, M. B. Platt, H. D. Platt, Probabilistic neural networks in bankruptcy prediction, J. Bus. Res., 44 (1999), 67–74. https://doi.org/10.1016/S0148-2963(97)00242-7 doi: 10.1016/S0148-2963(97)00242-7
[55]	Y. Qu, P. Quan, M. Lei, Y. Shi, Review of bankruptcy prediction using machine learning and deep learning techniques, Procedia Comput. Sci., 162 (2019), 895–899. https://doi.org/10.1016/j.procs.2019.12.065 doi: 10.1016/j.procs.2019.12.065
[56]	I. Guyon, J. Weston, S. Barnhill, Gene selection for cancer classification using support vector machine, Mach. Learn., 46 (2002), 389–422. https://doi.org/10.1023/A:1012487302797 doi: 10.1023/A:1012487302797
[57]	S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput., 9 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 doi: 10.1162/neco.1997.9.8.1735
[58]	D. N. T. How, C. K. Loo, K. S. M. Sahari, Behavior recognition for humanoid robots using long short-term memory, Int. J. Adv. Robot. Syst., 13 (2016), 1–14. https://doi.org/10.1177/1729881416663369 doi: 10.1177/1729881416663369
[59]	H. Palangi, R. Ward, L. Deng, Distributed compressive sensing: A deep learning approach, IEEE Trans. Signal Process., 64 (2016), 4504–4518. https://doi.org/10.1109/TSP.2016.2557301 doi: 10.1109/TSP.2016.2557301
[60]	W. Bao, J. Yue, Y. Rao, A deep learning framework for financial time series using stacked autoencoders and long-short term memory, PLoS ONE, 12 (2017), e0180944. https://doi.org/10.1371/journal.pone.0180944 doi: 10.1371/journal.pone.0180944
[61]	M. Schuster, K. K. Paliwal, Bidirectional recurrent neural networks, IEEE Trans. Signal Process., 45 (1997), 2673–2681. https://doi.org/10.1109/78.650093 doi: 10.1109/78.650093
[62]	T. Chen, R. Xu, Y. He, X. Wang, Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN, Expert Syst. Appl., 72 (2017), 221–230. https://doi.org/10.1016/j.eswa.2016.10.065 doi: 10.1016/j.eswa.2016.10.065
[63]	A. Graves, N. Jaitly, A. Mohamed, Hybrid speech recognition with deep bidirectional LSTM, Proc. IEEE Workshop Autom. Speech Recognit. Underst. (ASRU), (2013), 273–278. https://doi.org/10.1109/ASRU.2013.6707742 doi: 10.1109/ASRU.2013.6707742
[64]	A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, J. Schmidhuber, A novel connectionist system for unconstrained handwriting recognition, IEEE Trans. Pattern Anal. Mach. Intell., 31 (2009), 855–868. https://doi.org/10.1109/TPAMI.2008.137 doi: 10.1109/TPAMI.2008.137
[65]	Y. Fan, Y. Qian, F. L. Xie, F. K. Soong, TTS synthesis with bidirectional LSTM based recurrent neural networks, Proc. Interspeech, (2014). https://doi.org/10.21437/Interspeech.2014-443 doi: 10.21437/Interspeech.2014-443
[66]	Y. Lecun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Handwritten digit recognition with a back-propagation network, Adv. Neural Inf. Process. Syst., 2 (1989), 396–404.
[67]	Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, 86 (1998), 2278–2323. https://doi.org/10.1109/5.726791 doi: 10.1109/5.726791
[68]	D. Fuqua, T. Razzaghi, A cost-sensitive convolution neural network learning for control chart pattern recognition, Expert Syst. Appl., 150 (2020), 113275. https://doi.org/10.1016/j.eswa.2020.113275 doi: 10.1016/j.eswa.2020.113275
[69]	A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25 (2012).
[70]	Y. Zheng, Q. Liu, E. Chen, Y. Ge, J. L. Zhao, Time series classification using multi-channels deep convolutional neural networks, Lect. Notes Comput. Sci., 8485 (2014), 298–310. https://doi.org/10.1007/978-3-319-08010-9_33 doi: 10.1007/978-3-319-08010-9_33
[71]	V. Agarwal, R. Taffler, Comparing the performance of market-based and accounting-based bankruptcy prediction models, J. Banking Finance, 32 (2008), 1541–1551. https://doi.org/10.1016/j.jbankfin.2007.07.014 doi: 10.1016/j.jbankfin.2007.07.014
[72]	T. Chen, C. Guestrin, XGBoost: A scalable tree boosting system, Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., (2016), 785–794. https://doi.org/10.1145/2939672.2939785 doi: 10.1145/2939672.2939785
[73]	G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, et al., LightGBM: A highly efficient gradient boosting decision tree, Adv. Neural Inf. Process. Syst., 30 (2017), 3146–3154.
[74]	F. Chollet, Keras, 2015.
[75]	M. Abadi, A. Agarwal, P. Barham, et al., TensorFlow: Large-scale machine learning on heterogeneous distributed systems, arXiv preprint, arXiv: 1603.04467, 2015.
[76]	C. Elkan, The foundations of cost-sensitive learning, Proc. 17th Int. Joint Conf. Artif. Intell. (IJCAI), (2001), 973–978.
[77]	F. Provost, Machine learning from imbalanced data sets 101, Proc. AAAI'2000 Workshop Imbalanced Data Sets, (2000).
[78]	V. S. Sheng, C. X. Ling, Thresholding for making classifiers cost-sensitive, Proc. 21st Natl. Conf. Artif. Intell. (AAAI), (2006).
[79]	S. Nanda, P. Pendharkar, Linear models for minimizing misclassification costs in bankruptcy prediction, Intell. Syst. Account. Finance Manage., 10 (2001), 155–168. https://doi.org/10.1002/isaf.203 doi: 10.1002/isaf.203
[80]	S. M. Lundberg, S. I. Lee, A unified approach to interpreting model predictions, Adv. Neural Inf. Process. Syst., 30 (2017).
[81]	F. Jiménez, J. Palma, G. Sánchez, D. Marín, M. D. Francisco Palacios, M. D. Lucía López, Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction, Artif. Intell. Med., 104 (2020), 101818. https://doi.org/10.1016/j.artmed.2020.101818 doi: 10.1016/j.artmed.2020.101818
[82]	T. Niu, J. Wang, H. Lu, W. Yang, P. Du, Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting, Expert Syst. Appl., 148 (2020), 113237. https://doi.org/10.1016/j.eswa.2020.113237 doi: 10.1016/j.eswa.2020.113237

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