Abstract
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.
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Mousin, L., Jourdan, L., Kessaci Marmion, ME., Dhaenens, C. (2016). Feature Selection Using Tabu Search with Learning Memory: Learning Tabu Search. In: Festa, P., Sellmann, M., Vanschoren, J. (eds) Learning and Intelligent Optimization. LION 2016. Lecture Notes in Computer Science(), vol 10079. Springer, Cham. https://doi.org/10.1007/978-3-319-50349-3_10
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DOI: https://doi.org/10.1007/978-3-319-50349-3_10
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