On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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How to filter emails is a problem for Internet users. Support vector machine (SVM) is a valid filtering emails method. As it is well known, there exists uncertainty in deciding the legitimate email by Internet users. To formalize the uncertainty, the legitimate email is understood as fuzzy concept on a set of email samples in this paper, its membership function is obtained by aggregating opinions of Internet users, and aggregation operator is ordered weighted averaging (OWA) operator. Due to email training samples with membership degrees of the legitimate email, fuzzy support vector machine (FSVM) is adopted to classify emails, and penalty factor of FSVM is decided by content-specific misclassification costs. The advantages of our method are: 1) uncertainty of the legitimate email, i.e., membership degree, is considered in classifying emails, and a method to obtain membership degree is given; 2) content-specific misclassification costs is used to decide penalty factor of FSVM. Simulative experiments are shown to the effectiveness and human consistent of our method.