Abstract
This chapter discusses a genetic-algorithm-based approach for selecting a small number of instances from a given data set in a pattern classification problem. Our genetic algorithm also selects a small number of features. The selected instances and features are used as a reference set in a nearest neighbor classifier. Our goal is to improve the classification ability of our nearest neighbor classifier by searching for an appropriate reference set. We first describe the implementation of our genetic algorithm for the instance and feature selection. Next we discuss the definition of a fitness function in our genetic algorithm. Then we examine the classification ability of nearest neighbor classifiers designed by our approach through computer simulations on some data sets. We also examine the effect of the instance and feature selection on the learning of neural networks. It is shown that the instance and feature selection prevents the overfitting of neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Corcoran, A. L. and Sen, S. (1994). “Using real-valued genetic algorithms to evolve rule sets for classification”, Proc. of 1st IEEE International Conference on Evolutionary Computation, 120–124.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press.
Ishibuchi, H., Murata, T., and Turksen, I. B. (1997). “Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems”, Fuzzy Sets and Systems, 89: 135–150.
Ishibuchi, H. and Nakashima, T. (1999). “Evolution of reference sets in nearest neighbor classification”, in B. McKay et al.(eds.) Lecture Notes in Artificial Intelligence 1585: Simulated Evolution and Learning (2nd Asian-Pacific Conference on Simulated Evolution and Learning, Canberra, 1998, Selected Papers), 82–89.
Ishibuchi, H. and Nakashima, T. (2000). “Pattern and feature selection by genetic algorithms in nearest neighbor classification”, International Journal of Advanced Computational Intelligence to appear.
Kelly, Jr., J. D. and Davis, L. (1991). “Hybridizing the genetic algorithm and the k nearest neighbors classification algorithm”, Proc. of 14th International Conference on Genetic Algorithms, 377–383.
Kudo, M. and Sklansky, J. (2000). “Comparison of algorithms that select features for pattern classifiers”, Pattern Recognition, 33: 25–41.
Kuncheva, L. I. (1995). “Editing for the k-nearest neighbors rule by a genetic algorithm”, Pattern Recognition Letters, 16: 809–814.
Kuncheva, L. I. and Jain, L. C. (1999). “Nearest neighbor classifier: Simultaneous editing and feature selection”, Pattern Recognition Letters, 20: 1149–1156.
Punch, W. F., Goodman, E. D., Pei, M., Chia-Shun, L., Hovland, P., and Enbody, R. (1993). “Further research on feature selection and classification using genetic algorithms”, Proc. of 5th International Conference on Genetic Algorithms, 557–564.
Rumelhart, D. E., McClelland, J. L., and the PDP Research Group. (1986). Parallel Distributed Processing. Cambridge, MA: MIT Press.
Siedlecki, W. and Sklansky, J. (1989). “A note on genetic algorithms for large-scale feature selection”, Pattern Recognition Letters, 10: 335–347.
Skalak, D. B. (1994). “Prototype and feature selection by sampling and random mutation hill climbing algorithms”, Proc. of Eleventh International Conference on Machine Learning, 293–301.
Weiss, S. M. and Kulikowski, C. A. (1991). Computer Systems That Learn. San Mateo, CA: Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Ishibuchi, H., Nakashima, T., Nii, M. (2001). Genetic-Algorithm-Based Instance and Feature Selection. In: Liu, H., Motoda, H. (eds) Instance Selection and Construction for Data Mining. The Springer International Series in Engineering and Computer Science, vol 608. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3359-4_6
Download citation
DOI: https://doi.org/10.1007/978-1-4757-3359-4_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4861-8
Online ISBN: 978-1-4757-3359-4
eBook Packages: Springer Book Archive