Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A novel fuzzy compensation multi-class support vector machine
Applied Intelligence
An Improved Support Vector Machine for the Classification of Imbalanced Biological Datasets
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
A novel multi-class support vector machine based on fuzzy theories
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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Support vector machines just use the sign of decision value to get the decision class but don't take its value into consideration. Compared with the support vector machines, the proposed machine not only gives the decision class, but also the membership to each class using the decision value. For SVMs are essentially a 2-class classifier, we first construct the fuzzy output SVMs for 2-class, then extend it to multi-class case. In multi-class case, the feature space is divided into three parts: absolutely classified region, unclassified region and positive margin region because of different accuracy in them. In different regions, the range of the value of membership is different. Through the membership, we can get the location information of the data, which can tell us the confidence of the decision. So this will be helpful for further decision and analysis. The experiments show that the performance of fuzzy output SVMs is almost the same as the one-to-one approach, but when the membership to two classes is comparative and less than 0.8, the second maximal membership can sometimes correspond to the real class.