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Semi-supervised spam filtering using aggressive consistency learning

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SIGIR '10: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval

Pages 751 - 752

https://doi.org/10.1145/1835449.1835598

Published: 19 July 2010 Publication History

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Abstract

A graph based semi-supervised method for email spam filtering, based on the local and global consistency method, yields low error rates with very few labeled examples. The motivating application of this method is spam filters with access to very few labeled message. For example, during the initial deployment of a spam filter, only a handful of labeled examples are available but unlabeled examples are plentiful. We demonstrate the performance of our approach on TREC 2007 and CEAS 2008 email corpora. Our results compare favorably with the best-known methods, using as few as just two labeled examples: one spam and one non-spam.

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Cited By

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Saeejil NYounis HHayder IMuthmainnah Apriani EAditi Sharma MAl Yakin A(2024)Exploring Algorithmic Paradigms in Message Classification: Insights from the Enron E-mail DatasetAdvances in Information Communication Technology and Computing10.1007/978-981-97-6103-6_3(27-40)Online publication date: 2-Oct-2024
https://doi.org/10.1007/978-981-97-6103-6_3
Zavrak SYilmaz S(2023)Email spam detection using hierarchical attention hybrid deep learning methodExpert Systems with Applications10.1016/j.eswa.2023.120977233(120977)Online publication date: Dec-2023
https://doi.org/10.1016/j.eswa.2023.120977
Li WMeng WTan ZXiang Y(2014)Towards Designing an Email Classification System Using Multi-view Based Semi-supervised LearningProceedings of the 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications10.1109/TrustCom.2014.26(174-181)Online publication date: 24-Sep-2014
https://dl.acm.org/doi/10.1109/TrustCom.2014.26
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Index Terms

Semi-supervised spam filtering using aggressive consistency learning
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

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

General Chairs:
Fabio Crestani
University of Lugano, CH
,
Stéphane Marchand-Maillet
University of Geneva, CH
,
Program Chairs:
Hsin-Hsi Chen
National Taiwan University, TW
,
Efthimis N. Efthimiadis
University of Washington, USA
,
Jacques Savoy
University of Neuchatel, CH

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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SIGIR '10

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SIGIR

SIGIR '10: The 33rd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2010

Geneva, Switzerland

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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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Cited By

View all

Saeejil NYounis HHayder IMuthmainnah Apriani EAditi Sharma MAl Yakin A(2024)Exploring Algorithmic Paradigms in Message Classification: Insights from the Enron E-mail DatasetAdvances in Information Communication Technology and Computing10.1007/978-981-97-6103-6_3(27-40)Online publication date: 2-Oct-2024
https://doi.org/10.1007/978-981-97-6103-6_3
Zavrak SYilmaz S(2023)Email spam detection using hierarchical attention hybrid deep learning methodExpert Systems with Applications10.1016/j.eswa.2023.120977233(120977)Online publication date: Dec-2023
https://doi.org/10.1016/j.eswa.2023.120977
Li WMeng WTan ZXiang Y(2014)Towards Designing an Email Classification System Using Multi-view Based Semi-supervised LearningProceedings of the 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications10.1109/TrustCom.2014.26(174-181)Online publication date: 24-Sep-2014
https://dl.acm.org/doi/10.1109/TrustCom.2014.26
Whissell JClarke CPotdar V(2011)Clustering for semi-supervised spam filteringProceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference10.1145/2030376.2030391(125-134)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1145/2030376.2030391
ZAVRAK SYilmaz S(undefined)Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning MethodSSRN Electronic Journal10.2139/ssrn.4177036
https://doi.org/10.2139/ssrn.4177036
ZAVRAK SYilmaz S(undefined)Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning MethodSSRN Electronic Journal10.2139/ssrn.4177035
https://doi.org/10.2139/ssrn.4177035

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Clustering for semi-supervised spam filtering

Spam filter evaluation with imprecise ground truth

On the relative age of spam and ham training samples for email filtering