Publication: An Effective Metaheuristic for Bi-objective Feature Selection in Two-Class Classification Problem
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2019
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IOP Publishing Ltd
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Feature selection is known as a very useful technique in machine learning practice as it may result in the development of more straightforward models with better accuracy. Traditionally, feature selection is considered as a single-objective problem, however, it can be easily formulated in terms of two objectives. The solving of such problems requires the application of appropriate multi-objective optimization methods that do not always offer equally good solutions even under the same conditions. This paper focuses on the development of a metaheuristic optimization approach for bi-objective feature selection problem in two-class classification. We consider the solving of this problem in terms of minimization of both misclassification error and feature subset size. For solving the considered problem, an adaptation of the Multi-Objective Adaptive Memory Programming (MOAMP) metaheuristic based on the tabu search strategy is proposed. Our MOAMP adaption has been utilized to obtain the sets of most relevant features for two real classification problems with two classes. Finally, using popular Pareto front quality indicators, the obtained results have been compared with the sets of non-dominated solutions derived by the well-known NSGA2 algorithm. The conducted research allows concluding about the ability of the MOAMP adaptation to get a better efficient frontier for the same number of objective function calls.
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Attribution 4.0 International
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IE Business School
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Lyubchenko, A. A., Pacheco, J. A., Casado, S., & Nuñez, L. (2019, March). An effective metaheuristic for bi-objective feature selection in two-class classification problem. In Journal of Physics: Conference Series (Vol. 1210, No. 1, p. 012086). IOP Publishing.