A New Multi-class Fuzzy Support Vector Machine Algorithm
In this paper a novel approach to fuzzy support vector machines (SVM) in multi-class classification problems is presented. The proposed algorithm has the property to benefit from fuzzy labeled data in the training phase and can determine fuzzy memberships for input data. The algorithm can be considered as an extension of the traditional multi-class SVM for crisp labeled data, and it also extents the fuzzy SVM approach for fuzzy labeled training data in the two-class classification setting. Its behavior is demonstrated on three benchmark data sets, the achieved results motivate the inclusion of fuzzy labeled data into the training set for various tasks in pattern recognition and machine learning, such as the design of aggregation rules in multiple classifier systems, or in partially supervised learning.
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