Data reduction for instance-based learning using entropy-based partitioning

Authors:
Seung-Hyun Son;Jae-Yearn Kim
Affiliations:
Department of Industrial Engineering, Hanyang University, Seoul, South Korea;Department of Industrial Engineering, Hanyang University, Seoul, South Korea
Venue:
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Year:
2006

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Abstract

Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy.