Version 1
: Received: 26 November 2019 / Approved: 27 November 2019 / Online: 27 November 2019 (09:51:31 CET)
How to cite:
Assiri, A.S.; Velastin, S.; Nazir, S. A Hybrid Ensemble Method for Accurate Breast Cancer Tumor Classification using State-of-the-Art Classification Learning Algorithms. Preprints2019, 2019110341
Assiri, A.S.; Velastin, S.; Nazir, S. A Hybrid Ensemble Method for Accurate Breast Cancer Tumor Classification using State-of-the-Art Classification Learning Algorithms. Preprints 2019, 2019110341
Assiri, A.S.; Velastin, S.; Nazir, S. A Hybrid Ensemble Method for Accurate Breast Cancer Tumor Classification using State-of-the-Art Classification Learning Algorithms. Preprints2019, 2019110341
APA Style
Assiri, A.S., Velastin, S., & Nazir, S. (2019). A Hybrid Ensemble Method for Accurate Breast Cancer Tumor Classification using State-of-the-Art Classification Learning Algorithms. Preprints. https://doi.org/
Chicago/Turabian Style
Assiri, A.S., Sergio Velastin and Saima Nazir. 2019 "A Hybrid Ensemble Method for Accurate Breast Cancer Tumor Classification using State-of-the-Art Classification Learning Algorithms" Preprints. https://doi.org/
Abstract
Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to predict and classify breast cancer is very important. In this paper, a hybrid ensemble method classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms for the Wisconsin Breast Cancer Dataset (WBCD) were evaluated. 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, stochastic gradient descent learning 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.
Keywords
breast cancer tumor; classification; majority-based voting mechanism; multilayer perceptron learning network; simple logistic regression; stochastic gradient descent learning; wisconsin breast cancer dataset
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
