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.

An experimental analysis of the impact of accuracy degradation in SVM classification / D. Malchiodi. - In: INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE STUDIES. - ISSN 1755-4977. - 1:2(2009), pp. 163-190. [10.1504/IJCISTUDIES.2009.031345]

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

D. Malchiodi
Primo
2009

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.
Data quality ; accuracy degradation ; classification ; support vector classification ; machine learning ; accuracy degradation ; computational intelligence ; SVM, support vector machines ; classifiers ; quality measures
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/160373
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