Selection and classification of breast cancer diagnosis based on support vector machines

Abstract

Support Vector Machines (SVM) is a new algorithm of drain mining technique, recently received increasing popularity in machine learning community. This paper emphasizes how I-norm SVM can be used in feature selection and smooth SVM (SSVM) for classification. As a case study, a breast cancer diagnosis was implemented. First, feature selection for support vector machines was utilized to determine the important features. Then, SSVM was used to classify the stale of disease (benign or malignant) of breast cancer. As a result, SVM can achieve the state of the art performance on feature selection and classification.