강화된 진화 탐색이 생성하는 큰 자손 기반 효과적인 다중 레이블 특징 선별Effective Multi-label Feature Selection based on Large Offspring Set created by Enhanced Evolutionary Search Process
- Authors
- Lim, 1)Hyunki; Seo, Wangduk; Lee, Jaesung
- Issue Date
- Sep-2018
- Publisher
- 한국컴퓨터정보학회
- Keywords
- Multi-label Learning; Multi-label Feature Selection; Evolutionary Search; Memetic Offspring Creation
- Citation
- 한국컴퓨터정보학회논문지, v.23, no.9, pp 7 - 13
- Pages
- 7
- Journal Title
- 한국컴퓨터정보학회논문지
- Volume
- 23
- Number
- 9
- Start Page
- 7
- End Page
- 13
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/2496
- DOI
- 10.9708/jksci.2018.23.09.007
- ISSN
- 1598-849X
2383-9945
- 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.
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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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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