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Support vector machine classifier based on fuzzy partition and neighborhood pairs. (Chinese. English summary) Zbl 1172.68585

Summary: Support Vector Machine (SVM) is sensitive to noises and outliers. To overcome this drawback, Fuzzy Support Vector Machine (FSVM) is developed, in which the fuzzy membership function is set subjectively. In this study, support vector machine classifier based on fuzzy partition and neighborhood pairs (FPNP-SVC) is presented to deal with the classification problems with noises or outliers. In the proposed algorithm, fuzzy \(c\)-means clustering is firstly adopted to cluster each of two classes from the training set based on the clustering validity; Then \(c\) binary classification problems are formed based on the clustering results; Finally, based on neighborhood pairs strategy, for each sample a binary classifier constructed by two nearest subsets from two classes is chosen to identify it. The experiments are conducted on four benchmarking datasets for testing the generalization performance of FPNP-SVC. The experimental results show that FPNP-SVC is valid for improving the prediction accuracy of the classification problems with noises or outliers.

MSC:

68T05 Learning and adaptive systems in artificial intelligence
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