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.

[1]  Sheng-De Wang,et al.  Fuzzy support vector machines , 2002, IEEE Trans. Neural Networks.

[2]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[3]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[4]  S. Abe,et al.  Fuzzy support vector machines for pattern classification , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[5]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2004 .

[6]  Shigeo Abe,et al.  Fuzzy least squares support vector machines for multiclass problems , 2003, Neural Networks.

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  Chan-Yun Yang.  Support Vector Classifier with a Fuzzy-Value Class Label , 2004, ISNN.

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[11]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[12]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .