A Fuzzy Model With Online Incremental SVM and Margin-Selective Gradient Descent Learning for Classification Problems

Abstract

This paper proposes a new incremental learning approach to endow a Takagi-Sugeno-type fuzzy classification model with high generalization ability. The proposed fuzzy model is learned through incremental support vector machine (SVM) and margin-selected gradient descent learning and is called FM3. In this learning approach, training samples are fed incrementally one-by-one instead of all in one batch. The FM3 evolves from an empty rule set. A one-pass clustering algorithm is used to determine the number of rules and initial fuzzy sets in the rule antecedent part. An online incremental linear SVM is proposed to tune the rule consequent parameters to endow the FM3 with high generalization ability. The use of incremental instead of batch SVM enables the FM3 to handle online training problems with only one new sample available at a time. For antecedent parameter learning, a margin-selected gradient descent algorithm is proposed to prevent overtraining. Simulation results and comparisons with SVMs and fuzzy classifiers with different learning algorithms demonstrate the advantage of the FM3.