Serial Number: http://www.riss.kr/link?id=A107383285

Title: Effective Multi-population Genetic Algorithm using Migrant Refinement for Multi-label Humanity Data

Author: Park minwoo, Jaesung Lee

Journal: Journal of AI Humanities (JAIH)

Vol: 7

Pages: 259-280

Date: 2021.

Register Information: KCI

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<Abstract>

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.