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Using a similarity measure for credible classification

Anthony, Martin, Hammer, P. L., Subasi, E. and Subasi, M. (2005) Using a similarity measure for credible classification. CDAM research report series (CDAM-LSE-2005-22). Centre for Discrete and Applicable Mathematics, London School of Economics and Political Science, London, UK.

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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.

Item Type: Monograph (Report)
Official URL:
Additional Information: © 2005 the authors
Divisions: Mathematics
Subjects: Q Science > QA Mathematics
Date Deposited: 23 Oct 2008 09:43
Last Modified: 15 Sep 2023 22:05

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