Training algorithms for fuzzy support vector machines with noisy data

Fuzzy support vector machines (FSVMs) provide a method to classify data with noises or outliers. Each data point is associated with a fuzzy membership that can reflect their relative degrees as meaningful data. In this paper, we investigate and compare two strategies of automatically setting the fuzzy memberships of data points. It makes the usage of FSVMs easier in the application of reducing the effects of noises or outliers. The experiments show that the generalization error of FSVMs is comparable to other methods on benchmark datasets.