Abstract
Feature selection is considered one of the challenging machine learning tasks. Selecting a subset of relevant features can significantly influence on the classification accuracy and computational time of any machine learning algorithm. This paper introduces a novel wrapper-based feature selection algorithm based on using Equilibrium Optimizer (EO) algorithm and chaos theory. The principles of chaos theory is used to overcome the slow convergence rate and the entrapment in local optima problems of the original EO. Thus, ten different chaotic maps are embedded in the optimization process of EO to overcome these problems and achieve a more effective and robust search mechanism. Also, eight different S-shaped and V-shaped transfer functions are employed. The performance of the proposed hybrid algorithm is tested on fifteen benchmark datasets and four other large scale NLP datasets collected from the UCI machine learning repository. The experimental results showed the capability of the proposed hybrid algorithm. Moreover, the results proved that the proposed hybrid algorithm is a higly competitive algorithm and can find the optimal feature subset, which minimizes the number of selected features while maximizes the classification accuracy.
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Appendix A. List of benchmark functions
Appendix A. List of benchmark functions
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Sayed, G.I., Khoriba, G. & Haggag, M.H. A novel Chaotic Equilibrium Optimizer Algorithm with S-shaped and V-shaped transfer functions for feature selection. J Ambient Intell Human Comput 13, 3137–3162 (2022). https://doi.org/10.1007/s12652-021-03151-7
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DOI: https://doi.org/10.1007/s12652-021-03151-7