关键词: 模糊分割, 聚类有效性, 支持向量机, 噪声点, 异常点

Abstract: 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,a Fuzzy Partition based
Support Vector machine Classifier（FP-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 nearest pairs of clusters are searched,which form c binary classification problems;Finally,the weighted average strategy is applied to these c binary classifiers for inducing the final classification results.The experiments are conducted on three benchmarking UCI datasets for testing the generalization performance of FP-SVC.The experimental results show that FP-SVC is valid for improving the predicting accuracy of the classification problems with noises or outliers.

Key words: fuzzy partition, clustering validity, Support Vector Machine（SVM）, noise, outlier