Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000028-5.doi: 10.11896/jsjkx.211000028

• Information Security • Previous Articles     Next Articles

Application of Improved Feature Selection Algorithm in Spam Filtering

LI Yong-hong1, WANG Ying1, LI La-quan1, ZHAO Zhi-qiang2   

  1. 1 School of Science,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
    2 School of Software Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:LI Yong-hong,born in 1970,B.S,professor.His main research interests include combinatorial optimization,fuzzy matroid and data processing.
  • Supported by:
    National Natural Science Foundation of China(2020YFC2003502,61876201,61901074),Natural Science Foundation Project of Chongqing(cstc2020jcyj-msxmX0649) and Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN201900636).

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Abstract: Spam usually refers to e-mails with promotional materials,viruses and other contents that are forcibly sent to the user’se-mail address without user’s request.It has the characteristics of batch sending,and will cause great harm on the Internet.Therefore,it is very important to filter out these spams for users.The essence of the spam filtering problem is a text classification problem,which has a very high features dimension.But not all features contribute to classification,so choosing a suitable subset of features that can reflect the entire data set is the basis for constructing a good email classifier.Existing feature selection me-thods have some limitations,such as redundancy between features,unstable result of feature reduction and high computational cost.By studying and analyzing some of the advantages and disadvantages of the existing spam processing methods,a new integrated feature selection method based on the information gain method and the granular ball neighborhood rough set method is proposed,named IGBNRS algorithm.Through the experimental comparison on different classification models,the proposed algorithm simplifies the model and has a good performance.

Key words: Spam filtering, Feature selection, Attribute reduction, Text classification, IGGBNRS

CLC Number: 

  • TP391
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