A comparison of three different methods for classification of breast cancer data
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 methodol...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2008
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| Online Access: | https://eprints.nottingham.ac.uk/28136/ |
| _version_ | 1848793515480317952 |
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| author | Soria, Daniele Garibaldi, Jonathan M. Biganzoli, Elia M. Ellis, Ian O. |
| author_facet | Soria, Daniele Garibaldi, Jonathan M. Biganzoli, Elia M. Ellis, Ian O. |
| author_sort | Soria, Daniele |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:01:31Z |
| format | Conference or Workshop Item |
| id | nottingham-28136 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:31Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-281362020-05-04T20:27:57Z https://eprints.nottingham.ac.uk/28136/ A comparison of three different methods for classification of breast cancer data Soria, Daniele Garibaldi, Jonathan M. Biganzoli, Elia M. Ellis, Ian O. 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. 2008 Conference or Workshop Item PeerReviewed Soria, Daniele, Garibaldi, Jonathan M., 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. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4725039 |
| spellingShingle | Soria, Daniele Garibaldi, Jonathan M. Biganzoli, Elia M. Ellis, Ian O. A comparison of three different methods for classification of breast cancer data |
| title | A comparison of three different methods for classification of breast cancer data |
| title_full | A comparison of three different methods for classification of breast cancer data |
| title_fullStr | A comparison of three different methods for classification of breast cancer data |
| title_full_unstemmed | A comparison of three different methods for classification of breast cancer data |
| title_short | A comparison of three different methods for classification of breast cancer data |
| title_sort | comparison of three different methods for classification of breast cancer data |
| url | https://eprints.nottingham.ac.uk/28136/ https://eprints.nottingham.ac.uk/28136/ |