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A comparison of three different methods for classification of breast cancer data

Soria, Daniele; Garibaldi, Jonathan M.; Biganzoli, Elia M.; Ellis, Ian O.

Authors

Daniele Soria

Jonathan M. Garibaldi

Elia M. Biganzoli

Ian O. Ellis



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 naïve 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.

Publication Date Jan 1, 2008
Peer Reviewed Peer Reviewed
APA6 Citation Soria, D., Garibaldi, J. M., Biganzoli, E. M., & Ellis, I. O. (2008). A comparison of three different methods for classification of breast cancer data
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4725039
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: ICMLA 2008: Seventh International Conference on Machine Learning and Applications. Los Alamitos, Calif.: IEEE Computer Society, 2008. ISBN: 978-0-7695-3495-4, pp. 619-624, doi: 10.1109/ICMLA.2008.97.

© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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