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

Author

Listed:
  • Saba Bashir
  • Usman Qamar
  • Farhan Khan

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. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Saba Bashir & Usman Qamar & Farhan Khan, 2015. "Heterogeneous classifiers fusion for dynamic breast cancer diagnosis using weighted vote based ensemble," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(5), pages 2061-2076, September.
    • Handle: RePEc:spr:qualqt:v:49:y:2015:i:5:p:2061-2076
    DOI: 10.1007/s11135-014-0090-z
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    Citations

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    Cited by:

    1. Meshwa Rameshbhai Savalia & Jaiprakash Vinodkumar Verma, 2023. "Classifying Malignant and Benign Tumors of Breast Cancer: A Comparative Investigation Using Machine Learning Techniques," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 12(1), pages 1-19, January.
    2. Sultan Almotairi & Elsayed Badr & Mustafa Abdul Salam & Hagar Ahmed, 2023. "Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
    3. Liu, Qiang, 2021. "Reliability evaluation of two-stage evidence classification system considering preference and error," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Akampurira Paul & Mutebi Joe & Mugisha Brian & Muhaise Hussein & Kyomuhangi Rosette, 2024. "Exploring Dimensionality Reduction Techniques for Improved Breast Cancer Diagnosis," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(5), pages 808-824, May.
    5. Liu, Qiang & Zhang, Hailin, 2022. "Reliability evaluation of weighted voting system based on D–S evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    6. Wang, Haifeng & Zheng, Bichen & Yoon, Sang Won & Ko, Hoo Sang, 2018. "A support vector machine-based ensemble algorithm for breast cancer diagnosis," European Journal of Operational Research, Elsevier, vol. 267(2), pages 687-699.

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