An Approach to Email Classification Using Bayesian Theorem

Denil Vira; Dr. Denil Vira; Pradeep Raja

An Approach to Email Classification Using Bayesian Theorem

Denil Vira * §

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Pradeep Raja

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siddgada

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§ Mumbai University

An Approach to Email Classification Using Bayesian Theorem

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CST21238

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Abstract

Classifiers based on Bayesian theorem have been very effective in Spam filtering due to their strong categorization ability and high precision. This paper proposes an algorithm for email classification based on Bayesian theorem. The purpose is to automatically classify mails into predefined categories. The algorithm assigns an incoming mail to its appropriate category by checking its textual contents. The experimental results depict that the proposed algorithm is reasonable and effective method for email classification.

References

10 Cites in Article

Reference Format

M Sahami,S Dumais,D Heckerman,E Horvitz (1998). A Bayesian approach to filtering junk email.
I Androutsopoulos,G Paliouras,E Michelakis (2004). Learning to Filter Unsolicited Commercial E-Mail.
I Androutsopoulos,J Koutsias,K Chandrinos,G Paliouras,C Spyropoulos (2000). An Evaluation of Naïve Bayesian Anti-Spam Filtering.
Seongwook Youn,Dennis Mcleod (2007). Efficient Spam Email Filtering using Adaptive Ontology.
Shlomo Hershkop,Salvatore Stolfo (2005). Combining email models for false positive reduction.
Andrew Mccallum,Kamal Nigam (1998). A Comparison of Event Models for Naive Bayes Text Classification.
Wenjia Wang,Phillis Jones,Derek Partridge (2000). Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems.
R Beckermann,A Mccallum,G Huang (2004). Automatic categorization of email into folders: benchmark experiments on Enron and SRI corpora.
Fuchun Peng,Dale Schuurmans,Shaojun Wang (2004). Augmenting Naive Bayes Classifiers with Statistical Language Models.
David Lewis,William Gale (1994). A sequential algorithm for training text classfiers.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Denil Vira, Pradeep Raja, siddgada. 2012. "An Approach to Email Classification Using Bayesian Theorem". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 12 (GJCST Volume 12 Issue C13).

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GJCST Volume 12 Issue C13

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Bayesian Email classification keywords Probability text tokens

Classification

GJCST-C Classification E.5

Submission ReceivedDecember 13, 2011
Peer Review Double Blind
Handling Editor
Accepted January 3, 2012
Published January 15, 2012

Version of record

v1.2

Issue date

September 6, 2012

Language

English

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Article Matrices

Total Score: 148

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. Denil Vira, Pradeep Raja, Shidharth Gada (PhD/Dr. count: 1)

View Count (all-time): 517

Total Views (Real + Logic): 2529

Total Downloads (simulated): 144

Publish Date: 2012 01, Sun

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Total Views: 2.5K

Total Downloads: 144

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