Hybrid filter-wrapper feature selection using whale optimization algorithm: A multi-objective approach

Adel Got

doi:10.1016/j.eswa.2021.115312

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Hybrid filter-wrapper feature selection using whale optimization algorithm: A multi-objective approach

2021-11-01

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Adel Got

Abdelouahab Moussaouı

Djaafar Zouache *

DOI：10.1016/j.eswa.2021.115312

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Abstract

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Feature selection aims at finding the minimum number of features that result in high classification accuracy. Accordingly, the feature selection is considered as a multi-objective problem. However, most existing approaches treat feature selection as single-objective problem, and they are often divided into two main categories: filter and wrapper methods. Filters are known as fast methods but less accurate, while wrappers are computationally expensive but with high classification performance. This paper proposes a novel hybrid filter-wrapper feature selection approach using whale optimization algorithm (WOA). The proposed method is a multi-objective algorithm in which a filter and wrapper fitness functions are optimized simultaneously. Our algorithm's efficiency is demonstrated through an extensive comparison with seven well-known algorithms on twelve benchmark datasets. Experimental results show the ability of the proposed algorithm to obtain several subsets that include smaller number of features with excellent classification accuracy.

Keywords:

Feature selection

Filter and wrapper approaches

Multi-objective optimization

Whale optimization algorithm (WOA)

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