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다중 레이블 인문 데이터의 효과적인 특징 선별을 위한 이주 개체 정제연산 기반 다중 개체군 유전알고리즘
박민우 ( Park Minwoo ),이재성 ( Lee Jaesung ) 중앙대학교 인공지능인문학연구소 2021 인공지능인문학연구 Vol.7 No.-
Multi-label feature selection is a preprocessing method that can be used to analyze, for example multi-label humanity data. In particular, a multi-population genetic algorithm is verified to exhibit a better performance for identifying an appropriate subset compared with existing genetic algorithms in that a variety of populations was preserved, and premature convergence was prevented. However, with this method, the inflow of closely related features to multi-labels is unlikely to search for the solution. This study proposes an effective multi-population genetic algorithm for multi-label feature selection. In the proposed method, a multi-population genetic algorithm with a refinement process in migrated individuals maintains a variety of populations, promotes the inflow of features closely related to multi-labels, and ultimately enhances the search performance. Experimental results indicate that the proposed method exhibit better performance than the compared multi-population algorithms.
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