CLASSIFICATION OF MULTIPLE CANCER TYPES USING FUZZY SUPPORT VECTOR MACHINES AND OUTLIER DETECTION METHODS

The support vector machine (SVM) is a new learning method and has shown comparable or better results than the neural networks on some applications. In this paper, we applied SVM to classify multiple cancer types by gene expression profiles and exploit some strategies of the SVM method, including fuzzy logic and statistical theories. Using the proposed strategies and outlier detection methods, the FSVM (fuzzy support vector machine) can achieve a comparable or better performance than other methods, and provide a more flexible architecture to discriminate against SRBCT and non-SRBCT samples.