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Multi-field learning for email spam filtering

Published: 19 July 2010 Publication History

Abstract

Through the investigation of email document structure, this paper proposes a multi-field learning (MFL) framework, which breaks the multi-field document Text Classification (TC) problem into several sub-document TC problems, and makes the final category prediction by weighted linear combination of several sub-document TC results. Many previous statistical TC algorithms can be easily rebuilt within the MFL framework via turning binary result to spamminess score, which is a real number and reflects the likelihood that the classified email is spam. The experimental results in the TREC spam track show that the performances of many TC algorithms can be improved within the MFL framework.

References

[1]
Gordon V. Cormack. Email spam filtering: a systematic review. Foundations and Trends in Information Retrieval, 1(4):335--455, 2008.
[2]
H. Drucker, D. Wu, V. N. Vapnik. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5):1048--1054, 1999.
[3]
Gordon V. Cormack. TREC 2007 spam track overview. In TREC2007: Proceedings of the 16th Text REtrieval Conference, National Institute of Standards and Technology, Special Publication 500--274, 2007.
[4]
Paul Graham. Better bayesian filtering. In Proceedings of the 2003 Spam Conference, January 2003.
[5]
D. Sculley, Gabriel M. Wachman. Relaxed online SVMs for spam filtering. In SIGIR'07: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 415--422, 2007.
[6]
Thomas G. Dietterich. Ensemble methods in machine learning. In MCS2000: Proceedings of the Multiple Classifier Systems, pages 1--15, 2000.

Cited By

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  • (2016)Structural learning framework for binary short text classification2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603347(1188-1193)Online publication date: Aug-2016
  • (2014)Probabilistic ensemble learning for vietnamese word segmentationProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609477(931-934)Online publication date: 3-Jul-2014
  • (2012)Online active multi-field learning for efficient email spam filteringKnowledge and Information Systems10.1007/s10115-011-0461-x33:1(117-136)Online publication date: 1-Oct-2012
  • Show More Cited By

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    cover image ACM Conferences
    SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    July 2010
    944 pages
    ISBN:9781450301534
    DOI:10.1145/1835449
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 19 July 2010

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    Author Tags

    1. multi-field learning
    2. spam filtering
    3. text feature selection

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    SIGIR '10 Paper Acceptance Rate 87 of 520 submissions, 17%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    View all
    • (2016)Structural learning framework for binary short text classification2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)10.1109/FSKD.2016.7603347(1188-1193)Online publication date: Aug-2016
    • (2014)Probabilistic ensemble learning for vietnamese word segmentationProceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval10.1145/2600428.2609477(931-934)Online publication date: 3-Jul-2014
    • (2012)Online active multi-field learning for efficient email spam filteringKnowledge and Information Systems10.1007/s10115-011-0461-x33:1(117-136)Online publication date: 1-Oct-2012
    • (2010)Online supervised learning from multi-field documents for email spam filtering2010 International Conference on Machine Learning and Cybernetics10.1109/ICMLC.2010.5580676(3335-3340)Online publication date: Jul-2010

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