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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Son, S. H., & Kim, J. Y. (2006). Data reduction for instance-based learning using entropy-based partitioning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3982 LNCS, pp. 590–599). Springer Verlag. https://doi.org/10.1007/11751595_63
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