Bayesian Spam Filtering Using Statistical Data Compression

α
Dr. GUMPINA V V SATYA PRASAD
Dr. GUMPINA V V SATYA PRASAD
σ
SATYA P KUMAR SOMAYAJULA
SATYA P KUMAR SOMAYAJULA
α Andhra University Andhra University
σ Jawaharlal Nehru Technological University, Kakinada Jawaharlal Nehru Technological University, Kakinada

Send Message

To: Author

Bayesian Spam Filtering Using Statistical Data Compression

Article Fingerprint

ReserarchID

02P4M

Bayesian Spam Filtering Using Statistical Data Compression Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Abstract

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.

References

9 Cites in Article
  1. H Almuallim,T Dietterich (1991). Learning with many irrelevant features.
  2. I Androutsopoulos,G Paliouras,V Karkaletsis,G Sakkis,C Spyropoulos,P Stamatopoulos (2000). Learning to filter spam email: A comparison of a naive bayesian and a memory-based approach.
  3. Ion Androutsopoulos,John Koutsias,Konstantinos Chandrinos,Constantine Spyropoulos (2000). An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages.
  4. I Androutsopoulos,G Paliouras,E Michelakis,L Breiman,P Spector (1992). Submodel selection and evaluation in regression: The Xrandom case.
  5. G Androutsopoulos,E Paliouras,Michelakis (2004). Learning to filter unsolicited commercial e-mail.
  6. Fidelis Assis,William Yerazunis,Christian Siefkes,Shalendra Chhabra (2005). CRM114 versus Mr. X: CRM114 Notes for the TREC 2005 Spam Track.
  7. A Barron,J Rissanen,B Yu (1998). The minimum description length principle in coding and modeling.
  8. D Benedetto,E Caglioti (2002). December Bayesian Spam Filtering Using Statistical Data Compression and Loreto V. Language trees and zipping.
  9. A Bratko,B Filipi Spam filtering using character-level markov models: Experiments for the TREC 2005 Spam Track.

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. GUMPINA V V SATYA PRASAD. 2011. \u201cBayesian Spam Filtering Using Statistical Data Compression\u201d. Global Journal of Research in Engineering - I: Numerical Methods GJRE-I Volume 11 (GJRE Volume 11 Issue I7): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Version of record

v1.2

Issue date

December 28, 2011

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 5789
Total Downloads: 2941
2026 Trends
Related Research

Published Article

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.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Bayesian Spam Filtering Using Statistical Data Compression

Dr. GUMPINA V V SATYA PRASAD
Dr. GUMPINA V V SATYA PRASAD Andhra University
SATYA P KUMAR SOMAYAJULA
SATYA P KUMAR SOMAYAJULA Jawaharlal Nehru Technological University, Kakinada

Research Journals