Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction

Finding and removing misclassified instances are important steps in data mining and machine learning that affect the performance of the data mining algorithm in general. In this paper, we propose a C-Support Vector Classification Filter (C-SVCF) to identify and remove the misclassified instances (outliers) in breast cancer survivability samples collected from Srinagarind hospital in Thailand, to improve the accuracy of the prediction models. Only instances that are correctly classified by the filter are passed to the learning algorithm. Performance of the proposed technique is measured with accuracy and area under the receiver operating characteristic curve (AUC), as well as compared with several popular ensemble filter approaches including AdaBoost, Bagging and ensemble of SVM with AdaBoost and Bagging filters. Our empirical results indicate that C-SVCF is an effective method for identifying misclassified outliers. This approach significantly benefits ongoing research of developing accurate and robust prediction models for breast cancer survivability.