The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classification of a novel dataset and perform a methodological comparison of these. We used the C4.5 tree classifier, a Multilayer Perceptron and a naive Bayes classifier over a large set of tumour markers. We found good performance of the Multilayer Perceptron even when we reduced the number of features to be classified. We found naive Bayes achieved a competitive performance even though the assumption of normality of the data is strongly violated.

A Comparison of Three Different Methods for Classification of Breast Cancer Data / D. Soria, J. M. Garibaldi, E. Biganzoli, I. O. Ellis - In: 2008 Seventh International Conference on Machine Learning and Applications[s.l] : IEEE Computer Society Washington, DC, USA, 2008. - ISBN 978-0-7695-3495-4. - pp. 619-624

A Comparison of Three Different Methods for Classification of Breast Cancer Data

E. Biganzoli
Penultimo
;
2008

Abstract

The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classification of a novel dataset and perform a methodological comparison of these. We used the C4.5 tree classifier, a Multilayer Perceptron and a naive Bayes classifier over a large set of tumour markers. We found good performance of the Multilayer Perceptron even when we reduced the number of features to be classified. We found naive Bayes achieved a competitive performance even though the assumption of normality of the data is strongly violated.
Settore MED/01 - Statistica Medica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/188932
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