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

Soria, Daniele and Garibaldi, Jonathan M. and Biganzoli, Elia M. and Ellis, Ian O. (2008) A comparison of three different methods for classification of breast cancer data. In: Machine Learning and Applications 2008 (ICMLA'08) Seventh International Conference on Seventh International Conference on Machine Learning and Applications, 11-13 Dec 2008, San Diego, California, USA.

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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.

Item Type: Conference or Workshop Item (Paper)
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.
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Depositing User: Soria, Dr Daniele
Date Deposited: 18 Mar 2015 15:07
Last Modified: 15 Sep 2016 05:28
URI: http://eprints.nottingham.ac.uk/id/eprint/28136

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