Title :
Fuzzy Support Vector Machines Based on Convex Hulls
Author :
Liu, Hongbing ; Xiong, Shengwu ; Chen, Qiong
Abstract :
Fast fuzzy support vector machines (FFSVMs) based on the convex hulls are proposed in this paper. Firstly, the convex hull of each class data is generated by using the quick hull algorithm, and the data points lying inside the convex hull are not important to form FSVMs and then discarded. Secondly, the reduced training set consisting of the convex points is used to train the FFSVMs. Thirdly, the benchmark two-class problems and multi-class problems datasets are used to test the effectiveness and validness of FFSVMs. The experiment results indicate that FFSVMs not only reduce the training set but also achieve the same or better performance compared with the traditional FSVMs.
Keywords :
computational geometry; fuzzy set theory; learning (artificial intelligence); pattern classification; support vector machines; computational geometry; convex hull algorithm; fuzzy support vector machine training; pattern classification; Acceleration; Clustering algorithms; Clustering methods; Computer science; Costs; Machine learning; Pattern recognition; Support vector machine classification; Support vector machines; Training data; convex hulls; fast fuzzy support vector machines; fuzzy support vector machines; support vector machines;
Conference_Titel :
Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-3530-2
Electronic_ISBN :
978-1-4244-3531-9
DOI :
10.1109/KAMW.2008.4810642
