A novel multi population based particle swarm optimization for feature selection
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Feature selection is an integral part of any machine learning system and the success of such systems highly depends on the relevance of features with the target domain. Feature selection can be classified as NP-Hard problem since a large number of possible solutions exists especially when the feature space is high dimensional. In addition to standard feature selection algorithms, evolutionary algorithms have also yielded promising results. In this paper, a novel multi population based particle swarm optimization (MPPSO) is proposed for feature selection. In this method, multi population start with initial solutions generated by random and Relieff based initialization and searches solution space simultaneously using both populations. 26 UCI and 3 ASU datasets are used to evaluate the performance of the method. The results show that MPPSO generally achieves better average classification accuracies than the other algorithms. Specifically, for the datasets with a large number of features, MPPSO achieves the smallest number of selected features with highest classification accuracies compared to other algorithms. (c) 2021 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Particle swarm optimization, Multi-population initialization, Meta-heuristics, Transfer functions
Kaynak
Knowledge-Based Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
219
