Section: New Results

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

Participants: C. Dhaenens, L. Jourdan, M-E. Kessaci

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. 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 conducted show the benefit of using a learning mechanism to solve hard instances of the literature [hal-01370396v1].