Bayesian Spam Filtering using Statistical Data Compression

The Spam e-mail has become a major problem for companies and private users. This paper associated with spam and some different approaches attempting to deal with it. The most appealing methods are those that are easy to maintain and prove to have a satisfactory performance. Statistical classifiers a...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of computer science and information security Vol. 9; no. 10; p. 157
Main Authors Sudhakar, V, Rao, C P V N J Mohan, Somayajula, Satya Pavan Kutnar
Format Journal Article
LanguageEnglish
Published Pittsburgh L J S Publishing 01.10.2011
Subjects
Bayesian analysis
Classification
Classifiers
Data compression
Electronic mail
Electronic mail systems
Email
Filtering
Learning
Probability distribution
Random variables
Spamming
Studies
Online AccessGet full text

Cover

More Information
Summary:The Spam e-mail has become a major problem for companies and private users. This paper associated with spam and some different approaches attempting to deal with it. The most appealing methods are those that are easy to maintain and prove to have a satisfactory performance. Statistical classifiers are such a group of methods as their ability to filter spam is based upon the previous knowledge gathered through collected and classified e-mails. A learning algorithm which uses the Naive Bayesian classifier has shown promising results in separating spam from legitimate mail. [PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1947-5500