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Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble

Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based... Ensemble classifiers provide an efficient method to deal with diverse set of applications in various domains. The proposed research signifies the effectiveness of ensemble classifier for computer-aided breast cancer diagnosis. A novel combination of five heterogeneous classifiers namely Naïve Bayes, Decision tree using Gini index, Decision tree using information gain, Support vector machine and Memory based learner are used to make an ensemble framework. Weighted voting technique is used to determine the final prediction where weights are assigned on the basis of classification accuracy. Four different breast cancer datasets are used from online data repositories. Feature selection and various preprocessing techniques are applied on the datasets to enhance the classification accuracy. The analyses of experimental results show that the proposed ensemble technique provided a significant improvement as compared to other classifiers. The best accuracy achieved by proposed ensemble is 97.42 % whereas the best precision and recall is 100 and 98.60 % respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble

Bashir, Saba; Qamar, Usman; Khan, Farhan
Quality & Quantity , Volume 49 (5) – Aug 19, 2014
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References (36)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
DOI
10.1007/s11135-014-0090-z
Publisher site
See Article on Publisher Site

Abstract

Ensemble classifiers provide an efficient method to deal with diverse set of applications in various domains. The proposed research signifies the effectiveness of ensemble classifier for computer-aided breast cancer diagnosis. A novel combination of five heterogeneous classifiers namely Naïve Bayes, Decision tree using Gini index, Decision tree using information gain, Support vector machine and Memory based learner are used to make an ensemble framework. Weighted voting technique is used to determine the final prediction where weights are assigned on the basis of classification accuracy. Four different breast cancer datasets are used from online data repositories. Feature selection and various preprocessing techniques are applied on the datasets to enhance the classification accuracy. The analyses of experimental results show that the proposed ensemble technique provided a significant improvement as compared to other classifiers. The best accuracy achieved by proposed ensemble is 97.42 % whereas the best precision and recall is 100 and 98.60 % respectively.

Journal

Quality & Quantity – Springer Journals

Published: Aug 19, 2014

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