Serial Number : http://www.riss.kr/link?id=A105596093

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

Author : 임현기(Hyun-Ki LIM), 서왕덕(Wang-Duk SEO), 이재성(Jae-Sung LEE)

Journal : 韓國컴퓨터情報學會論文誌 (Journal of the Korea society of computer and information)

Vol. : 23 No.9 [2018]

Pages  7-13

Publishded by : 한국컴퓨터정보학회 (Korea Society of Computer Information)

Date : 2018.09

Reigster Information : KCI

etc. :

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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.

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