IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v49y2015i5p2061-2076.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-014-0090-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-014-0090-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. Liu, Qiang, 2021. "Reliability evaluation of two-stage evidence classification system considering preference and error," Reliability Engineering and System Safety, Elsevier, vol. 213(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:49:y:2015:i:5:p:2061-2076. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.