NDLI: A Web Text Filter Based on Rough Set Weighted Bayesian

Content Provider	IEEE Xplore Digital Library
Author	Yu Wu Kun She Zhu, W. Xiaojun Yue Huiqiong Luo
Copyright Year	2009
Abstract	With the deep penetration of the Internet, uncontrolled flood of information has become one of the most serious problems to Internet users. Harmful contents about pornography, violence and other illegal messages, etc have posed serious influence to the whole society, especially to the young people. In this paper, a novel web text filter based on rough set and Bayesian theory is proposed to analysis text content of web pages to filter harmful pages. Some of current feature selection methods such as Inverse document frequency (IDF) does not take the classification information into account. To avoid this shortcoming rough set is used to reduce original feature terms. Meanwhile, a novel coefficient weighted method based on rough set is proposed and introduced into Bayesian formula, which will greatly improve filtering performance. In the final experiment, this paper compared the novel method with other weighted methods applied in Bayesian formula, such as Tf, IDF and TFIDF. The results demonstrate that this novel filter works efficiently.
Starting Page	241
Ending Page	245
File Size	272234
Page Count	5
File Format	PDF
ISBN	9780769539294
e-ISBN	9781424454211
DOI	10.1109/DASC.2009.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probability Information filtering Rough set Uniform resource locators Baysian theory Bayesian methods Web pages Information filters Set theory Feature extraction Filtering theory Internet web text filter
Content Type	Text
Resource Type	Article

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