Memetic feature selection: benchmarking hybridization schemata

Authors:
M. A. Esseghir;Gilles Goncalves;Yahya Slimani
Affiliations:
University of Lille Nord de France, F-59000, Lille, Artois University, LGI2A Laboratory, France;University of Lille Nord de France, F-59000, Lille, Artois University, LGI2A Laboratory, France;Sciences Faculty of Tunis, Tunis El-Manar University
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

Citing 6
Cited 0

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Abstract

Feature subset selection is an important preprocessing and guiding step for classification The combinatorial nature of the problem have made the use of evolutionary and heuristic methods indispensble for the exploration of high dimensional problem search spaces In this paper, a set of hybridization schemata of genetic algorithm with local search are investigated through a memetic framework Empirical study compares and discusses the effectiveness of the proposed local search procedure as well as their components.