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Using a similarity measure for credible classification

Subasi, M., Subasi, E., Anthony, Martin and Hammer, P. L. (2009) Using a similarity measure for credible classification. Discrete Applied Mathematics, 157 (5). pp. 1104-1112. ISSN 0166-218X

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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 observations. Explicitly, we use a new measure of how similar a point xset membership, variant{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 accuracies 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 accuracies with those obtained by restricting the domain on which classification is given by using the Hamming distance.

Item Type: Article
Official URL: http://www.sciencedirect.com/science/journal/01662...
Additional Information: © 2008 Elsevier B.V.
Library of Congress subject classification: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Sets: Departments > Mathematics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 18 Feb 2009 12:39
URL: http://eprints.lse.ac.uk/22720/

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