Abstract
This paper describes the application of a multi-population genetic algorithm to the selection of feature subsets for classification problems. The multi-population genetic algorithm based on the independent evolution of different subpopulations is to prevent premature convergence of each subpopulation by migration. Experimental results with UCI standard data sets show that multi-population genetic algorithm outperforms simple genetic algorithm.
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References
Cantú-Paz, E.: A survey of parallel genetic algorithms. Reseaux et Systems Repartis 10, 141–171 (1998)
Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 1, 131–156 (1997)
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Jain, A., Zongkerm, D.: Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 153–158 (1997)
Kudo, M., Sklansky, K.: Comparison of algorithms that select features for pattern classifiers. Pattern Recognition 33, 25–41 (2000)
Kuncheva, L.I., Jain, L.C.: Nearest Neighbor Classifier: Simultaneous Editing and Feature Selection. Pattern Recognition Letters 20, 1149–1156 (1999)
Wen-Yang, L., Tzung-Pei, H., Shu-Min, L.: On adapting migration parameters for multi-population genetic algorithms. In: Proceedings of IEEE International Conference on Systems, Man, Cybernetics, pp. 5731–5735 (2004)
Murphy, P.M., Aha, D.W.: UCI Repository of machine learning databases. University of California, Irivne, Department of Information and Computation Science (1996)
Narendra, P.M., Fukunaga, K.: A Branch and Bound Algorithm for Feature Subset Selection. IEEE Trans. Computers 26, 917–922 (1977)
Pudil, P., Ferri, F.J., Novovičová, J., Kittler, J.: Floating Search Methods in Feature Selection. Pattern Recognition Letters, 1119–1125 (1994)
Potts, J.C., Giddens, T.D., Yadav, S.B.: The Development and Evaluation of an Improved Genetic Algorithm Based on Migration and Artificial Selection. IEEE Transactions on Systems, Man, and Cybernetics 24, 73–86 (1994)
Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K.: Dimensionality Reduction Using Genetic Algorithms. IEEE Trans. Evolutionary Computation 4, 164–171 (2000)
Siedlecki, W., Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters 10, 335–347 (1989)
Jihoon, Y., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13, 44–49 (1998)
Bin, Y., Baozong, Y.: A More Efficient Branch and Bound Algorithm for Feature Selection. Pattern Recognition 26, 883–889 (1993)
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Zhu, H., Jiao, L., Pan, J. (2006). Multi-population Genetic Algorithm for Feature Selection. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_59
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DOI: https://doi.org/10.1007/11881223_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
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