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The optimal distance measure for a given discrimination task under the nearest neighbor framework has been shown to be the likelihood that a pair of measurements have different class labels [S. Mahamud et al., (2002)]. For implementation and efficiency considerations, the optimal distance measure was approximated by combining more elementary distance measures defined on simple feature spaces. We address two important issues that arise in practice for such an approach: (a) What form should thedoi:10.1184/r1/6555548.v1 fatcat:6o5i2if7tfh2xcqqv2cffenswi