Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process

  • Lim, Hyunki (Korea Institute of Science and Technology) ;
  • Seo, Wangduk (School of Computer Science and Engineering, Chung-Ang University) ;
  • Lee, Jaesung (School of Computer Science and Engineering, Chung-Ang University)
  • Received : 2018.06.28
  • Accepted : 2018.08.17
  • Published : 2018.09.28
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Abstract

Recent advancement in data gathering technique improves the capability of information collecting, thus allowing the learning process between gathered data patterns and application sub-tasks. A pattern can be associated with multiple labels, demanding multi-label learning capability, resulting in significant attention to multi-label feature selection since it can improve multi-label learning accuracy. However, existing evolutionary multi-label feature selection methods suffer from ineffective search process. In this study, we propose a evolutionary search process for the task of multi-label feature selection problem. The proposed method creates large set of offspring or new feature subsets and then retains the most promising feature subset. Experimental results demonstrate that the proposed method can identify feature subsets giving good multi-label classification accuracy much faster than conventional methods.

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References

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