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Using a similarity measure for credible classification

By Martin Anthony, P. L. Hammer, E. Subasi and M. Subasi

Abstract

This paper concerns classification by Boolean functions. We investigate the classification accuracy obtained by standard classification techniques on unseen points (elements of the domain, {0, 1}n, for some n) that are similar, in particular senses, to the points that have been observed as training obser- vations. Explicitly, we use a new measure of how similar a point x ∈ {0, 1}n is to a set of such points to restrict the domain of points on which we offer a classification. For points sufficiently dissimilar, no classification is given. We report on experimental results which indicate that the classification ac- curacies obtained on the resulting restricted domains are better than those obtained without restriction. These experiments involve a number of standard data-sets and classification techniques. We also compare the classification ac- curacies with those obtained by restricting the domain on which classification is given by using the Hamming distance

Topics: QA Mathematics
Publisher: Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science
Year: 2005
OAI identifier: oai:eprints.lse.ac.uk:13927
Provided by: LSE Research Online
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