Feature Selection and Ensemble Method Analysis for Breast Cancer Datasets
DOI:
https://doi.org/10.26438/ijcse/v10i4.1115Keywords:
J48, NaïveBayes, KNN, Voting classifier, feature selectionAbstract
Breast cancer has become the most common cause of death in women. Early detection of breast cancer helps out to reduce the risk factors. Three classification algorithms (NB, DT, and KNN) were used on two different Breast cancer datasets using the WEKA tool. The main purpose of this paper is to compare the results of the classification algorithms using voting and feature selection methods. The experimental result shows that voting of three classifiers gives the highest performance accuracy on the Breast cancer dataset. The ensemble method is used to increase the accuracy of the data mining algorithms. We also compare the performance accuracy of classifiers using feature selection methods (IG and PCA) on breast cancer datasets.
References
[1] Ahmed Iqbal Pritom, Shahed Anzarus Sabab, Md. Ahadur Rahman Munshi, Shihabuzzaman Shihab,"Predicting breast cancer recurrence using effective classification and feature selection technique", 19th International conference on computer and information technology, December 18-20, 2016.
[2] D. Lavanya,K. U. Rani, "Analysis of feature selection with classification: Breast Cancer Datasets", Indian Journal of computer science and engineering (IJCSE), Vol. 2 NO. 5 Oct-NOV 2011, ISSN: 0976-5166.
[3] G. Ravi Kumar, Dr. G. A. Ramachandra,K.Nagamani, "An Efficient Prediction of Breast Cancer Data using Data Mining Techniques", International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2 Issue 4 August 2013, ISSN: 2319-1058.
[4] Vikas Chaurasia, Saurabh Pal, "A Novel Approach for Breast Cancer Detection using Data Mining Techniques.", International Journal of Innovative Research in Computer and Communication Engineering ,Vol. 2, Issue 1, January 2014.
[5] U. Karthik Kumar, M.B. Sai Nikhil and K. Sumangali, "Prediction of Breast Cancer using Voting Classifier Technique", IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2 - 4 pp.108-114, August 2017.
[6] Subrato Bharati, Mohammad Atikur Rahman, Prajoy Podder, "Breast Cancer Prediction Applying Different Classification Algorithm with Comparative Analysis using WEKA.", 4th International conference on electrical engineering and information and communication technology, 2018.
[7] Vikas Chaurasia, Saurabh Pal and BB Tiwari, "Prediction of benign and malignant breast cancer using data mining techniques.", Journal of Algorithms & Computational Technology, Vol. 12(2) 119–126, 2018, doi:DOI: 10.1177/1748301818756225.
[8] V.Nanda Gopal, Fadi Al-Turjman, R. Kumar, L.Anand, M.Rajesh, "Feature selection and classification in breast cancer prediction usingIOTandmachine learning.", Elsevier, 18 April 2021, doi:https://doi.org/10.1016/j.measurement.2021.109442.
[9] Ghanchi, Nileshkumar Modi and Kaushar, "A Comparative Analysis of Feature Selection Methods and Associated Machine Learning Algorithms on Wisconsin Breast Cancer Dataset", Proceedings of International Conference on ICT for Sustainable Development, Advances in Intelligent Systems and Computing 408, 2016, doi:10.1007/978-981-10-0129-1_23.
[10] J. Han, M.K.,"Data Mining Concepts and Techniques", A volume of The Morgan Kaufmann Series in Data Management system, 2012.
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