@conference {ElOuassif2021352,
	title = {Homogeneous ensemble based support vector machine in breast cancer diagnosis},
	booktitle = {HEALTHINF 2021 - 14th International Conference on Health Informatics; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021},
	year = {2021},
	note = {cited By 5},
	pages = {352-360},
	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{\textquoteright}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 {\textcopyright} 2021 by SCITEPRESS {\textendash} Science and Technology Publications, Lda. All rights reserved},
	keywords = {Base 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},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103824221\&partnerID=40\&md5=2340dec93a42872a9ece0b6f6b9fccbc},
	author = {El Ouassif, B. and Idri, A. and Hosni, M.}
}
@article {ElOuassif2021263,
	title = {Investigating Accuracy and Diversity in Heterogeneous Ensembles for Breast Cancer Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12950 LNCS},
	year = {2021},
	note = {cited By 2},
	pages = {263-281},
	abstract = {Breast Cancer (BC) is one of the most common forms of cancer among women. Detecting and accurately diagnosing breast cancer at an early phase increase the chances of women{\textquoteright}s survival. For this purpose, various single classification techniques have been investigated to diagnosis BC. Nevertheless, none of them proved to be accurate in all circumstances. Recently, a promising approach called ensemble classifiers have been widely used to assist physicians accurately diagnose BC. Ensemble classifiers consist on combining a set of single classifiers by means of an aggregation layer. The literature in general shows that ensemble techniques outperformed single ones when ensemble members are accurate (i.e. have the lowest percentage error) and diverse (i.e. the single classifiers make uncorrelated errors on new~instances). Hence, selecting ensemble members is often a crucial task since it can lead to the opposite: single techniques outperformed their ensemble. This paper evaluates and compares ensemble members{\textquoteright} selection based on accuracy and diversity with ensemble members{\textquoteright} selection based on accuracy only. A comparison with ensembles without member selection was also performed. Ensemble performance was assessed in terms of accuracy, F1-score. Q statistics diversity measure was used to calculate the classifiers diversity. The experiments were carried out on three well-known BC datasets available from online repositories. Seven single classifiers were used in our experiments. Skott Knott test and Borda Count voting system were used to assess the significance of the performance differences and rank ensembles according to theirs performances. The findings of this study suggest that: (1) Investigating both accuracy and diversity to select ensemble members often led to better performance, and (2) In general, selecting ensemble members using accuracy and/or diversity led to better ensemble performance than constructing ensembles without members{\textquoteright} selection. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Breast Cancer, Combining classifiers, Computer aided diagnosis, Diseases, Diversity measure, Ensemble members, Ensemble-classifier, Heterogeneous ensembles, Member selection, Performance, Selection based, Voting, Voting machines},
	doi = {10.1007/978-3-030-86960-1_19},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125278232\&doi=10.1007\%2f978-3-030-86960-1_19\&partnerID=40\&md5=58285ac3fe1b29564700bef136a94771},
	author = {El Ouassif, B. and Idri, A. and Hosni, M.}
}
@conference {Idri2018,
	title = {A systematic map of data analytics in breast cancer},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2018},
	doi = {10.1145/3167918.3167930},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044739616\&doi=10.1145\%2f3167918.3167930\&partnerID=40\&md5=4cfbf5c917bfa294988a996c72ff2234},
	author = {Idri, A. and Chlioui, I. and El Ouassif, B.}
}
