Efficient Hybrid Nature-Inspired Binary Optimizers for Feature Selection
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The process of dimensionality reduction is a crucial solution to deal with the dimensionality problem that may be faced when dealing with the majority of machine learning techniques. This paper proposes an enhanced hybrid metaheuristic approach using grey wolf optimizer (GWO) and whale optimization algorithm (WOA) to develop a wrapper-based feature selection method. The main objective of the proposed technique is to alleviate the drawbacks of both algorithms, including immature convergence and stagnation to local optima (LO). The hybridization is done with improvements in the mechanisms of both algorithms. To confirm the stability of the proposed approach, 18 well-known datasets are employed from the UCI repository. Furthermore, the classification accuracy, number of selected features, fitness values, and run time matrices are collected and compared with a set of well-known feature selection approaches in the literature. The results show the superiority of the proposed approach compared with both GWO and WOA. The results also show that the proposed hybrid technique outperforms other state-of-the-art approaches, significantly.
KeywordsWhale optimization algorithm Grey wolf optimizer Optimization Feature selection Metaheuristics
This research was supported by the research committee at Birzeit University with a grant number 250177.
Compliance with Ethical Standards
Conflict of interests
The authors declare that they have no conflict of interest.
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).
Human and Animal Rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
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