Authors
Hisao Ishibuchi, Tomoharu Nakashima, Manabu Nii
Publication date
2001
Journal
Instance selection and construction for data mining
Pages
95-112
Publisher
Springer US
Description
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 …
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Scholar articles
H Ishibuchi, T Nakashima, M Nii - Instance selection and construction for data mining, 2001