Feature Selection using Tabu Search with Learning Memory: Learning Tabu Search

Lucien Mousin 1, 2, * Laetitia Jourdan 1, 2 Marie-Eléonore Marmion 1, 2 Clarisse Dhaenens 1, 2
* Auteur correspondant
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Feature selection in classification can be modeled as a com-binatorial 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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Communication dans un congrès
Learning and Intelligent OptimizatioN Conference LION 10, May 2016, Ischia Island (Napoli), Italy. 〈http://www.lion10.unina.it/〉
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Lucien Mousin, Laetitia Jourdan, Marie-Eléonore Marmion, Clarisse Dhaenens. Feature Selection using Tabu Search with Learning Memory: Learning Tabu Search. Learning and Intelligent OptimizatioN Conference LION 10, May 2016, Ischia Island (Napoli), Italy. 〈http://www.lion10.unina.it/〉. 〈hal-01370396〉

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