Name
Abstract | ||
|---|---|---|
The previous study of fuzzy support vector machines (FSVMs) provides a method to classify data with noises or outliers by manually associating each data point with a fuzzy membership that can reflect their relative degrees as meaningful data. In this paper, we introduce two factors in training data points, the confident factor and the trashy factor, and automatically generate fuzzy memberships of training data points from a heuristic strategy by using these two factors and a mapping function. We investigate and compare two strategies in the experiments and the results show that the generalization error of FSVMs is comparable to other methods on benchmark datasets. The proposed approach for automatic setting of fuzzy memberships makes the FSVMs more applicable in reducing the effects of noises or outliers. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.patrec.2004.06.009 | Pattern Recognition Letters |
Keywords | DocType | Volume |
fuzzy membership,automatic setting,meaningful data,data point,support vector machines,noisy data,training data point,generalization error,fuzzy support vector machine,optimization and classification,benchmark datasets,training algorithm,confident factor,noise,trashy factor,fuzzy set theory,data analysis | Journal | 25 |
Issue | ISSN | Citations |
14 | Pattern Recognition Letters | 66 |
PageRank | References | Authors |
2.54 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-Fu Lin | 1 | 514 | 30.39 |
| Sheng-De Wang | 2 | 720 | 68.13 |