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Authors: Bouchra El Ouassif 1 ; Ali Idri 2 ; 1 and Mohamed Hosni 3 ; 1

Affiliations: 1 Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco ; 2 MSDA, Mohammed VI Polytechnic University, Ben Guerir, Morocco ; 3 MOSI, L2M3S, ENSAM-Meknes, Moulay Ismail University, Meknes, Morocco

Keyword(s): Breast Cancer, Classification, Support Vector Machine (SVM), SVM Ensemble, Combined Kernel.

Abstract: 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. (More)

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Paper citation in several formats:
El Ouassif, B.; Idri, A. and Hosni, M. (2021). Homogeneous Ensemble based Support Vector Machine in Breast Cancer Diagnosis. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, ISBN 978-989-758-490-9; ISSN 2184-4305, pages 352-360. DOI: 10.5220/0010230403520360

@conference{healthinf21,
author={Bouchra {El Ouassif}. and Ali Idri. and Mohamed Hosni.},
title={Homogeneous Ensemble based Support Vector Machine in Breast Cancer Diagnosis},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,},
year={2021},
pages={352-360},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010230403520360},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,
TI - Homogeneous Ensemble based Support Vector Machine in Breast Cancer Diagnosis
SN - 978-989-758-490-9
IS - 2184-4305
AU - El Ouassif, B.
AU - Idri, A.
AU - Hosni, M.
PY - 2021
SP - 352
EP - 360
DO - 10.5220/0010230403520360

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