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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Son, SH., Kim, JY. (2006). Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3982. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751595_63
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DOI: https://doi.org/10.1007/11751595_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34075-1
Online ISBN: 978-3-540-34076-8
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