Chun-Fu LinSHENG-DE WANG2018-09-102018-09-102004-0101678655http://scholars.lib.ntu.edu.tw/handle/123456789/310876The 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. © 2004 Elsevier Ltd. All rights reserved.application/pdfapplication/pdfAlgorithms; Conformal mapping; Database systems; Error analysis; Heuristic methods; Learning systems; Optimization; Pattern recognition; Problem solving; Datasets; Fuzzy membership; Optimization and classification; Support vector machines (SVM); Fuzzy setsjournal article10.1016/j.patrec.2004.06.0092-s2.0-4644290661WOS:000224489200012