A Multi-SVM Fusion Model Using Type-2 FLS
Xiujuan Chen, Robert W. Harrison, Yanqing Zhang, Yu Qiu · 2006
Support vector machine (SVM) classification often heavily relies on selected kernel functions. This paper proposes a fuzzy fusion model to combine multi-SVMs to improve the performance of SVM classification. In order to better handle uncertainties in real classification applications, we apply type-2 fuzzy sets to create the fusion model. The model takes the classification results from multi-SVMs and generates the combined decision. Our experiments show the proposed model outperforms individual SVMs in most cases and also has better performance than type-1 based fusion model in general.