Title: An experimental analysis of the impact of accuracy degradation in SVM classification

Authors: D. Malchiodi

Addresses: Department of Computer Science, University of Milan, Via Comelico 39/41, 20135 Milano, Italy

Abstract: The aim of this paper is to analyse the phenomenon of accuracy degradation in the samples given as input to SVM classification algorithms. In particular, the effect of accuracy degradation on the performance of the learnt classifiers is investigated and compared, if possible, with theoretical results. The study shows how a family of SVM classification algorithms enhanced in order to deal with quality measures on the available data handles accuracy degradation better than the classical SVM approaches to classification.

Keywords: data quality; accuracy degradation; classification; support vector classification; machine learning; accuracy degradation; computational intelligence; SVM; support vector machines; classifiers; quality measures.

DOI: 10.1504/IJCISTUDIES.2009.031345

International Journal of Computational Intelligence Studies, 2009 Vol.1 No.2, pp.163 - 190

Published online: 01 Feb 2010 *

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