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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning
verfasst von : Seung-Hyun Son, Jae-Yearn Kim
Erschienen in: Computational Science and Its Applications - ICCSA 2006
Verlag: Springer Berlin Heidelberg
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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.