SVM Approach to Breast Cancer Classification

Abstract

The purpose of the proposed study was to provide a solution to the Wisconsin Diagnostic Breast Cancer (WDBC) classification problem. The WDBC dataset, provided by the University of Wisconsin Hospital, was derived from a group of images using Fine Needle Aspiration (biopsies) of the breast. An ensemble of Support Vector Machines (SVM's) was employed in this study. Support vectors with linear, polynomial and RBF kernel functions were trained using a fraction of the WDBC dataset as a training set. The five top performing models were recruited into the ensemble. The classification was then carried out using the majority opinion of the ensemble. The SVM ensemble successfully classified more than 99 percent of the testing data and in the process yielded 100 percent benign tumor prediction accuracy. Keywords: Support Vector Machines; Breast Cancer; Machine Learning, Ensemble, Classifiers

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