Refereed full papers (journals, book chapters, international conferences)

1997

@article{WiM1997,
	vgclass =	{refpap},
	author =	{D. Randall Wilson and Tony R. Martinez},
	title =	{Improved Heterogeneous Distance Functions},
	journal =	{Journal of Artificial Intelligence Research},
	volume =	{6},
	pages =	{1--34},
	month =	{January},
	year =	{1997},
	abstract =	{Instance-based learning techniques typically handle
	continuous and linear input values well, but often do not handle
	nominal input attributes appropriately. The Value Difference Metric
	(VDM) was designed to find reasonable distance values between nominal
	attribute values, but it largely ignores continuous attributes,
	requiring discretization to map continuous values into nominal values.
	This paper proposes three new heterogeneous distance functions, called
	the Heterogeneous Value Difference Metric (HVDM), the Interpolated
	Value Difference Metric (IVDM), and the Windowed Value Difference
	Metric (WVDM).  These new distance functions are designed to handle
	applications with nominal attributes, continuous attributes, or both.
	In experiments on 48 applications the new distance metrics achieve
	higher classification accuracy on average than three previous distance
	functions on those datasets that have both nominal and continuous
	attributes.},
}