Homogeneous ensemble based support vector machine in breast cancer diagnosis

TitreHomogeneous ensemble based support vector machine in breast cancer diagnosis
Publication TypeConference Paper
Year of Publication2021
AuthorsB. Ouassif, E, Idri, A, Hosni, M
Conference NameHEALTHINF 2021 - 14th International Conference on Health Informatics; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021
Mots-clésBase classifiers, Biomedical engineering, Breast cancer diagnosis, Classification models, Combination rules, Computer aided diagnosis, Diseases, Medical informatics, Multilayer neural networks, Multilayer Perceptron (MLP) classifier, Online repositories, Polynomial kernels, Radial basis function kernels, Support vector machines

Breast Cancer (BC) is one of the most common forms of cancer and one of the leading causes of mortality among women. Hence, detecting and accurately diagnosing BC at an early stage remain a major factor for women's long-term survival. To this aim, numerous single techniques have been proposed and evaluated for BC classification. However, none of them proved to be suitable in all situations. Currently, ensemble methods have been widely investigated to help diagnosis BC and consists on generating one classification model by combining more than one single technique by means of a combination rule. This paper evaluates homogeneous ensembles whose members are four variants of the Support Vector Machine (SVM) classifier. The four SVM variants used four different kernels: Linear Kernel, Normalized Polynomial Kernel, Radial Basis Function Kernel, and Pearson VII function based Universal Kernel. A Multilayer Perceptron (MLP) classifier is used for combining the outputs of the base classifiers to produce a final decision. Four well-known available BC datasets are used from online repositories. The findings of this study suggest that: (1) ensembles provided a very promising performance compared to its base, and (2) there is no SVM ensemble with a combination of kernels that have better performance in all datasets. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved




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