Support vector machine classifier based on fuzzy partition and neighborhood pairs
基于模糊分割和邻近对的支持向量机分类器

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) was presented to deal with the classification problems with noises or outliers. In the proposed algorithm, fuzzy c-means clustering was firstly adopted to cluster each of two classes from the training set based on the clustering validity; Then c binary classification problems were 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 was chosen to identify it. The experiments were 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.