The Efficiency Comparison of Data Classification Ensemble Techniques for Breast Cancer Patients Dataset

Abstract

The objectives of this research are to 1) study the majority vote ensemble and bootstrap aggregating techniques, and 2) compare the efficiency of the ensemble data classification using 4 data mining techniques including k-nearest neighbor, artificial neural network, decision tree, and support vector machine: with the majority vote ensemble and bootstrap aggregating techniques for classification of breast cancer patient data. The results of the study show that the method that gives the best performance is majority vote and bootstrap aggregating of the artificial neural network by giving a classification accuracy of 97.66, a recall of 93.75 and a precision of 96.4. Followed by the artificial neural network give an accuracy of 97.08, a recall of 93.75 and a precision of 98.36.