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

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

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.

Citation

Soria, D., Garibaldi, J. M., Biganzoli, E. M., & Ellis, I. O. A comparison of three different methods for classification of breast cancer data. Presented at Machine Learning and Applications 2008 (ICMLA'08) Seventh International Conference on Seventh International Conference on Machine Learning and Applications

• Conference Name: Machine Learning and Applications 2008 (ICMLA'08) Seventh International Conference on Seventh International Conference on Machine Learning and Applications
• End Date: Dec 13, 2008
• Publication Date: Jan 1, 2008
• Deposit Date: Mar 18, 2015
• Publicly Available Date: Mar 18, 2015
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1016210
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4725039
• 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.