Technical Reports

1994

@techreport{Fri1994,
	vgclass =	{report},
	author =	{Jerome H. Friedman},
	title =	{Flexible Metric Nearest Neighbor Classification},
	institution =	{Department of Statistics and Stanford Linear
	Accelerator Center},
	address =	{Stanford University, Stanford, CA 94305, USA},
	month =	{November},
	year =	{1994},
	url =	{http://www-stat.stanford.edu/\~{}jhf/ftp/flexmet.pdf},
	abstract =	{The K-nearest-neighbor decision rule assigns an object of
	unknown class to the plurality class among the K labeled "training"
	objects that are closest to it. Closeness is usually defined in terms
	of a metric distance on the Euclidean space with the input measurement
	variables as axes. The metric chosen to define this distance can
	strongly effect performance. An optimal choice depends on the problem
	at hand as characterized by the respective class distributions on the
	input measurement space, and within a given problem, on the location of
	the unknown object in that space. In this paper new types of
	K-nearest-neighbor procedures are described that estimate the local
	relevance of each input variable, or their linear combinations, for
	each individual point to be classified. This information is then used
	to separately customize the metric used to define distance from that
	object in finding its nearest neighbors. These procedures are a hybrid
	between regular K-nearest-neighbor methods and tree-structured
	recursive partitioning techniques popular in statistics and machine
	learning.},
}