Name
Abstract | ||
|---|---|---|
As a machine-learning tool, support vector machines (SVMs) have been gaining popularity due to their promising performance. However, the generalization abilities of SVMs often rely on whether the selected kernel functions are suitable for real classification data. To lessen the sensitivity of different kernels in SVMs classification and improve SVMs generalization ability, this paper proposes a fuzzy fusion model to combine multiple SVMs classifiers. To better handle uncertainties existing in real classification data and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS), we apply interval type-2 fuzzy sets to construct a type-2 SVMs fusion FLS. This type-2 fusion architecture takes considerations of the classification results from individual SVMs classifiers and generates the combined classification decision as the output. Besides the distances of data examples to SVMs hyperplanes, the type-2 fuzzy SVMs fusion system also considers the accuracy information of individual SVMs. Our experiments show that the type-2 based SVM fusion classifiers outperform individual SVM classifiers in most cases. The experiments also show that the type-2 fuzzy logic-based SVMs fusion model is better than the type-1 based SVM fusion model in general. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.asoc.2007.02.019 | Appl. Soft Comput. |
Keywords | Field | DocType |
svms generalization ability,support vector machine,multiple svms classifier,type-2 svms fusion,real classification data,classifier fusion,type-2 fuzzy logic-based classifier,fuzzy logic-based svms fusion,individual svms,svms classification,support vector machines (svms),classification,type-2 fls,fuzzy logic,svms hyperplanes,type-2 fuzzy svms fusion,individual svms classifier,machine-learning,kernel function,membership function,machine learning | Classifier fusion,Pattern recognition,Fuzzy classification,Fuzzy logic,Support vector machine,Fuzzy set,Artificial intelligence,Hyperplane,Machine learning,Mathematics,Fusion system,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
8 | 3 | Applied Soft Computing Journal |
Citations | PageRank | References |
29 | 1.16 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiujuan Chen | 1 | 41 | 5.25 |
| Yong Li | 2 | 36 | 3.08 |
| Robert Harrison | 3 | 51 | 4.58 |
| Yan-Qing Zhang | 4 | 851 | 65.22 |