NDLI: Rough set and its application in Chinese spam filtering

Content Provider	IEEE Xplore Digital Library
Author	Yan Xu
Copyright Year	2011
Description	Author affiliation: Information and Science College, Beijing Language and Culture University, Beijing, China (Yan Xu)
Abstract	Feature selection plays an important role in text categorization. Automatic feature selection methods such as document frequency thresholds (DF), information gain (IG), mutual information (MI), and so on are commonly applied in text categorization. Existing experiments show IG is one of the most effective methods. In this paper, a feature selection method is proposed based on Rough Set theory and according to Rough set theory, knowledge about a universe of objects may be defined as classifications based on certain properties of the objects, i.e. rough set theory assume that knowledge is an ability to partition objects. We quantify the ability of classify objects, and call the amount of this ability as knowledge quantity and then following this quantification we put forward a notion ”knowledge Gain” and propose a knowledge gain feature selection method (KG method)The task of spam filtering can be seen as a special problem of text classification. An effective and efficient feature selection method is important, which can be easily and effectively select the major features to attain the goal for anti-spam filtering. We explore 2 classifiers (Naive Bayes and SVM), and run our experiments on Chinese-spam collection show that KG performs better than the IG method, specially, on extremely aggressive reduction. We conclude that the KG feature method have a state-of-the-art performance for filtering spam, especially for Chinese spam emails.
Starting Page	750
Ending Page	755
File Size	736118
Page Count	6
File Format	PDF
ISBN	9781457703720
e-ISBN	9781457703713
DOI	10.1109/GRC.2011.6122692
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-08
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Accuracy Text categorization Unsolicited electronic mail Set theory rough set clustering spam filtering feature selection artificial intelligence
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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