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

•A novel multi-objective feature selection algorithm is proposed.•It hybridizes filter and wrapper models into whale optimization algorithm.•It has been validated through ten datasets and compared against five algorithms.•Yields solutions with fewer features and improves the classification accuracy....

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Veröffentlicht in:Expert systems with applications Jg. 183; S. 115312
Hauptverfasser: Got, Adel, Moussaoui, Abdelouahab, Zouache, Djaafar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 30.11.2021
Elsevier BV
Schlagworte:
Algorithms
Classification
Feature selection
Filter and wrapper approaches
Multi-objective optimization
Multiple objective analysis
Optimization
Optimization algorithms
Whale optimization algorithm (WOA)
Whale optimization algorithm (WOA)
Feature selection
Multi-objective optimization
Filter and wrapper approaches
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Zusammenfassung:•A novel multi-objective feature selection algorithm is proposed.•It hybridizes filter and wrapper models into whale optimization algorithm.•It has been validated through ten datasets and compared against five algorithms.•Yields solutions with fewer features and improves the classification accuracy. 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115312