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Open AccessArticle

Breast Tumor Classification Using an Ensemble Machine Learning Method

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College of Business, King Khalid University, Abha 62529, Saudi Arabia
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Department of Software Engineering, Fatima Jinnah Women University, The Mall Rawalpindi, Punjab 46000, Pakistan
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Applied Artificial Intelligence Research Group, Department of Computer Science, Universidad Carlos III de Madrid, 28270 Colmenarejo, Spain
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School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
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Zebra Technologies Corporation, London SE1 9LQ, UK
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Author to whom correspondence should be addressed.
J. Imaging 2020, 6(6), 39; https://doi.org/10.3390/jimaging6060039
Received: 18 March 2020 / Revised: 22 May 2020 / Accepted: 25 May 2020 / Published: 29 May 2020
Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to detect possible breast cancer is very important. In this paper, an ensemble classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms were evaluated for the Wisconsin Breast Cancer Dataset (WBCD). The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic regression learning, support vector machine learning with stochastic gradient descent optimization and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42%, as compared to the state-of-the-art algorithm for WBCD. View Full-Text
Keywords: breast cancer tumor; classification; majority-based voting mechanism; multilayer perceptron learning network; simple logistic regression; stochastic gradient descent learning; wisconsin breast cancer dataset breast cancer tumor; classification; majority-based voting mechanism; multilayer perceptron learning network; simple logistic regression; stochastic gradient descent learning; wisconsin breast cancer dataset
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Assiri, A.S.; Nazir, S.; Velastin, S.A. Breast Tumor Classification Using an Ensemble Machine Learning Method. J. Imaging 2020, 6, 39.

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