A fast fuzzy support vector machine based on information granulation

In order to improve the efficiency of fuzzy support vector machine training high-dimensional and large-scale dataset, a fast fuzzy support vector machine based on information granulation (FSVM-FIG) is proposed. Firstly, the training set is divided into some granules by fuzzy C-means, including pure granules and mixed granules. Since most support vectors are close to the border of two classes of samples, we believe that the support vectors must be in mixed granules, so we save only the mixed granules for new training set. In addition, because there are some noises and outliers on the border of two classes of samples, we use the k-nearest neighbor algorithm to remove noises and outliers. Finally, we use fuzzy support vector machine based on cluster hyperplane to train the final training set. Experimental results show that FSVM-FIG can not only improve the training efficiency of the training sets that contain noises and outliers, but also ensure a certain degree of prediction accuracy.

[1]  Tao Lin,et al.  Analog circuit fault diagnosis based on fuzzy support vector machine and kernel density estimation , 2010, 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).

[2]  Rui Zhang,et al.  An New Fuzzy Support Vector Machine for Binary Classification , 2012 .

[3]  Qiang He,et al.  Multi-class fuzzy support vector machine based on dismissing margin , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[4]  Ding Shifei,et al.  a fuzzy support vector machine algorithm with dual membership based on hypersphere , 2011 .

[5]  Xiaowei Yang,et al.  A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises , 2011, IEEE Transactions on Fuzzy Systems.

[6]  Ding Shi-fei.  Advances of Support Vector Machines(SVM) , 2011 .

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

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

[9]  Liu San-yang.  Fuzzy support vector machine based on cluster hyperplane , 2008 .

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

[11]  Zhao Min,et al.  Credit Risk Assessment Based on Fuzzy SVM and Principal Component Analysis , 2009, 2009 International Conference on Web Information Systems and Mining.

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

[13]  Liu San.  Fuzzy Support Vector Machine Method Based on Border Vector Extraction , 2008 .

[14]  Lei Li,et al.  A New Intrusion Detection System Based on Rough Set Theory and Fuzzy Support Vector Machine , 2011, 2011 3rd International Workshop on Intelligent Systems and Applications.

[15]  Vasile Palade,et al.  FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning , 2010, IEEE Transactions on Fuzzy Systems.