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作者:

Li, Wenbin (Li, Wenbin.) | Liu, Chunnian (Liu, Chunnian.) | Chen, Yiying (Chen, Yiying.)

收录:

EI Scopus

摘要:

The growing problem of unsolicited bulk e-mail, also known as 'spam', has generated a need for reliable anti-spam e-mail filters. We introduce seven filtering algorithms: Naive Bayesian (NB), Decision Tree (DT), AdaBoost, ANN, SVM, VSM and KNN. Design considerations and implementation issues of these filters are discussed, such as how to get cost-sensitive NB, SVM, VSM, KNN. Using two relatively large amounts of real personal Email data, a comprehensive comparative study based on a cost-sensitive measure we approved was conducted using above seven filters. The study includes the effect of feature subset size, training-corpus distribution, issues that have not been explored in previous experiments. The comparative results show that cost-sensitive filters such as NB, SVM, VSM and KNN have fewer count of misclassifying legitimate when relative parameters, feature subset size and training dataset's distribution are reasonable. © 2005 by International Federation for Information Processing.

关键词:

Adaptive boosting Artificial intelligence Decision trees Electronic mail filters Signal filtering and prediction Support vector machines

作者机构:

  • [ 1 ] [Li, Wenbin]Beijing University of Technology, Beijing 100022, China
  • [ 2 ] [Li, Wenbin]ShiJiaZhuang University of Economics, ShiJiaZhuang 050031, China
  • [ 3 ] [Liu, Chunnian]Beijing University of Technology, Beijing 100022, China
  • [ 4 ] [Chen, Yiying]ShiJiaZhuang University of Economics, ShiJiaZhuang 050031, China

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ISSN: 1868-4238

年份: 2005

卷: 187

页码: 325-334

语种: 英文

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