Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process (강화된 진화 탐색이 생성하는 큰 자손 기반 효과적인 다중 레이블 특징 선별)

HyunKi Lim (임현기); Seo Wangduk (서왕덕); Jaesung Lee (이재성)

doi:10.9708/jksci.2018.23.09.007

Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2018, 23(9), pp.7-13
DOI : 10.9708/jksci.2018.23.09.007
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : June 28, 2018
Accepted : August 17, 2018
Published : September 28, 2018

HyunKi Lim ¹, Seo Wangduk ², Jaesung Lee ²

¹한국과학기술연구원
²중앙대학교

Accredited

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.

KEYWORDS

Multi-label Learning, Multi-label Feature Selection, Evolutionary Search, Memetic Offspring Creation

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process

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KEYWORDS

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* References for papers published after 2023 are currently being built.