alexa Solving feature subset selection problem by a Parallel Scatter Search
Biomedical Sciences

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): Flix Garca Lpez, Miguel Garca Torres, Beln Melin Batista, Jos A Moreno Prez, J Marcos MorenoVega

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The aim of this paper is to develop a Parallel Scatter Search metaheuristic for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the initial set in order to classify future instances. We propose two methods for combining solutions in the Scatter Search metaheuristic. These methods provide two sequential algorithms that are compared with a recent Genetic Algorithm and with a parallelization of the Scatter Search. This parallelization is obtained by running simultaneously the two combination methods. Parallel Scatter Search presents better performance than the sequential algorithms.

This article was published in European Journal of Operational Research and referenced in International Journal of Biomedical Data Mining

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