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This paper presents a study on attributes reduction, comparing five discriminant analysis techniques: FisherFace, CLDA, DLDA, YLDA and KLDA. Attributes reduction has been applied to the problem of leather defect classification using four different classifiers: C4.5, kNN, Naïve Bayes and Support Vector Machines. The results of several experiments on the performance of discriminant analysis...
Spam sender detection based on email subject data is a complex large-scale text mining task. The dataset consists of email subject lines and the corresponding IP address of the email sender. A fast and accurate classifier is desirable in such an application. In this research, a highly scalable SVM modeling method, named Granular SVM with Random granulation (GSVM-RAND), is designed. GSVM-RAND applies...
As the rapid development of the Internet, the occurrence of more and more spam mails becomes harmful to users. Content-based spam filtering technologies become the mainstream anti-spam mail methods so far. Support vector machine (SVM), Bayes, windows and KNN are excellent ones of these technologies and they have advantages and disadvantages respectively. The common shortage of content-based methods...
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