A reduction technique for nearest-neighbor classification: Small groups of examples

Authors:
Miroslav Kubat;Martin Cooperson, Jr.
Affiliations:
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146, USA;Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504-4330, USA
Venue:
Intelligent Data Analysis
Year:
2001

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Abstract

An important issue in nearest-neighbor classifiers is how to reduce the size of large sets of examples. Whereas many researchers recommend to replace the original set with a carefully selected subset, we investigate a mechanism that creates three or more such subsets. The idea is to make sure that each of them, when used as a 1-NN subclassifier, tends to err in a different part of the instance space. In this case, failures of individuals can be corrected by voting. The costs of our example-selection procedure are linear in the size of the original training set and, as our experiments demonstrate, dramatic data reduction can be achieved without a major drop in classification accuracy.