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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Pages619-624
Number of pages6
DOIs
Publication statusPublished - 2008
Event7th International Conference on Machine Learning and Applications, ICMLA 2008 - San Diego, CA, United States
Duration: Dec 11 2008Dec 13 2008

Other

Other7th International Conference on Machine Learning and Applications, ICMLA 2008
CountryUnited States
CitySan Diego, CA
Period12/11/0812/13/08

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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