Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan/Endnote
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Shalendra Chhabra Articles by William S. Yerazunis Articles by Christian Siefkes

Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Shalendra Chhabra, William S. Yerazunis, Christian Siefkes, "Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas," Data Mining, IEEE International Conference on, pp. 347-350, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10031, author = {Shalendra Chhabra and William S. Yerazunis and Christian Siefkes}, title = {Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {347-350}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10031}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas SN - 0-7695-2142-8 SP347 EP350 A1 - Shalendra Chhabra, A1 - William S. Yerazunis, A1 - Christian Siefkes, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Shalendra Chhabra, UC Riverside, CA

William S. Yerazunis, MERL, Cambridge, Massachusetts

Christian Siefkes, GKVI/FU Berlin, Germany

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10031

In this paper we present a Markov Random Field model based approach to filter spam. Our approach examines the importance of the neighborhood relationship (MRF cliques) among words in an email message for the purpose of spam classification. We propose and test several different theoretical bases for weighting schemes among corresponding neighborhood windows. Our results demonstrate that unexpected side effects depending on the neighborhood window size may have larger accuracy impact than the neighborhood relationship effects of the Markov Random Field.

Citation:

Shalendra Chhabra, William S. Yerazunis, Christian Siefkes, "Spam Filtering using a Markov Random Field Model with Variable Weighting Schemas," icdm, pp.347-350, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.