Solving feature subset selection problem by a Parallel Scatter Search
European Journal of Operational ResearchPublished 13 October 2004
F. García López, Miguel García-Torres, Belén Melián-Batista, José Andrés Moreno Pérez, J. Marcos Moreno‐Vega
Citations187
SJR quartileQ1
SJR score2.24
SNIP2.62
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TL;DR
These methods provide two sequential algorithms that are compared with a recent Genetic Algorithm and with a parallelization of the Scatter Search that presents better performance than the sequential algorithms.
Abstract
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.
Keywords
Computer Science
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