Title :
Fuzzy support vector machines for pattern classification
Author :
Inoue, Takuya ; Abe, Shigeo
Author_Institution :
Graduate Sch. of Sci. & Technol., Kobe Univ., Japan
Abstract :
In conventional support vector machines (SVMs), an n-class problem is converted into n two-class problems. For the ith two-class problem we determine the optimal decision function which separates class i from the remaining classes. In classification, a datum is classified into class i only when the value of the ith decision function is positive. In this architecture, the datum is unclassifiable if the values of more than one decision function are positive or all the valves are negative. In the paper, to overcome this problem, we propose fuzzy support vector machines (FSVMs). Using the decision functions obtained by training the SVM, for each class, we define a truncated polyhedral pyramidal membership function. Since, for the data in the classifiable regions, the classification results are the same for the two methods, the generalization ability of the FSVM is the same as or better than that of the SVM. We evaluate our method for three benchmark data sets and demonstrate the superiority of the FSVM over the SVM
Keywords :
fuzzy set theory; learning automata; optimisation; pattern classification; classifiable regions; fuzzy support vector machines; generalization ability; n-class problem; optimal decision function; pattern classification; truncated polyhedral pyramidal membership function; two-class problems; Machine learning; Pattern classification; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939575