Abstract
Value Difference Metric (VDM) is one of the widely used distance functions to define the distance between a pair of instances with nominal attributes only. Many approaches have been proposed to improve the performance of VDM. In this paper, we focus on the attribute selection approach and propose another improved Value Difference Metric. We call it Selective Value Difference Metric (SVDM). In order to learn SVDM, we investigate the attribute independence assumption held by VDM and then single out two effective attribute selection methods for SVDM. The experimental results on 36 UCI benchmark datasets validate the effectiveness of the proposed SVDM.
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