Open AccessDifferential search algorithm for biojective feature selection
Author(s) -
HongJun Du,
JingJun Yao,
Gang Sun,
Ming Li,
Wuming Zhou,
Haiyue Yu,
ChangBiao Guo,
Zhang Shan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2031/1/012064
Subject(s) - curse of dimensionality , feature selection , differential evolution , computer science , selection (genetic algorithm) , feature (linguistics) , convergence (economics) , evolutionary algorithm , algorithm , population , optimization problem , optimization algorithm , mathematical optimization , artificial intelligence , machine learning , mathematics , philosophy , linguistics , demography , sociology , economics , economic growth
Feature selection (FS) is a complex optimization problem with important real-world applications. Generally, the main target of FS is to reduce the dimensionality of features and enhance the effectiveness of solving problems. Due to the population characteristics, various evolutionary algorithms (EAs) have been proposed to solve feature selection problems over the past decades. However, the majority of them only consider single-objective optimization, while many real-world problems have multiple objectives, therefore, it is need to design more suitable and effective EAs to deal with multi-objective FS. In this paper, a biobjective FS algorithm based on differential search algorithm (DSA) is designed to solve FS problems, which minimizes the number of selected features and maximizes the classification accuracy. The results of simulation experiments and statistical analysis on 15 classification datasets compared with other four state-of-the-art EAs show that the proposed DSA algorithm can not only obtain better optimization performance, but also achieve competitive convergence accuracy.