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The Heterogeneous Centered Difference Measure provides a new method for calculating dissimilarity in instance-based learning algorithms for data classification. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 1, 2010

A New Heterogeneous Dissimilarity Measure for Data Classification

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Abstract

Instance-based learning algorithms typically suffer influences of dissimilarity functions. The problem is frequently related to the Nearest Neighbor rules of these algorithms. This paper will introduce a new dissimilarity measure, called Heterogeneous Centered Difference Measure, which is tested over many known databases. The results are compared with other distance functions.

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