Feature selection in classification can be modeled as a combinatorial optimization problem. One of the main particularities of this problem is the large amount of time that may be needed to evaluate the quality of a subset of features. In this paper, we propose to solve this problem with a tabu search algorithm integrating a learning mechanism. To do so, we adapt to the feature selection problem, a learning tabu search algorithm originally designed for a railway network problem in which the evaluation of a solution is time-consuming. Experiments are conducted and show the benefit of using a learning mechanism to solve hard instances of the literature.
CITATION STYLE
Mousin, L., Jourdan, L., Marmion, M. E. K., & Dhaenens, C. (2016). Feature selection using tabu search with learning memory: Learning tabu search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10079 LNCS, pp. 141–156). Springer Verlag. https://doi.org/10.1007/978-3-319-50349-3_10
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