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Multiple-instance learning for text categorization based on semantic representation

1. Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University, Shanghai, 200062, China;
2. Beijing Electro-Mechanical Engineering Institute Beijing, 100074, China

The mate selection plays a key role in natural evolution process. Although a variety of mating strategies have been proposed in the community of evolutionary computation, the importance of mate selection has been ignored. In this paper, we propose a clustering based mate selection (CMS) strategy for evolutionary algorithms (EAs). In CMS, the population is partitioned into clusters and only the solutions in the same cluster are chosen for offspring reproduction. Instead of doing a whole new clustering process in each EA generation, the clustering iteration process is combined with the evolution iteration process. The combination of clustering and evolving processes benefits EAs by saving the cost to discover the population structure. To demonstrate this idea, a CMS utilizing the k-means clustering method is proposed and applied to a state-of-the-art EA. The experimental results show that the CMS strategy is promising to improve the performance of the EA.
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Keywords Evolutionary algorithm; mate selection; clustering

Citation: Jinyuan Zhang, Aimin Zhou, Guixu Zhang, Hu Zhang. Multiple-instance learning for text categorization based on semantic representation. Big Data and Information Analytics, 2017, 2(1): 77-86. doi: 10.3934/bdia.2017010


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