Fuzzy Theory Based Support Vector Machine Classifier

Support vector machine (SVM) has become a popular tool in the area of pattern recognition, combining support vector machines with other theories has been proposed as a new direction to improve classification performance. This paper applies fuzzy theory to support vector machines for classification. In the first phase, a fuzzy support vector machine is proposed for the classification of real-world data with noise, fuzzy membership to each data point of SVM and reformulates the SVM such that different input points can make different contributions to the each class. In the second phase, the SVM´s kernel´s parameters are calculated by the kernel´s parameters evaluation function. To investigate the effectiveness of the proposed fuzzy support vector machine classifier, it is applied to the given dataset, the experimental results confirm the superiority of the presented method to the traditional SVM classifier.