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Re-examination on lam% in spam filtering

Published: 19 July 2010 Publication History

Abstract

Logistic average misclassification percentage (lam%) is a key measure for the spam filtering performance. This paper demonstrates that a spam filter can achieve a perfect 0.00% in lam%, the minimal value in theory, by simply setting a biased threshold during the classifier modeling. At the same time, the overall classification performance reaches only a low accuracy. The result suggests that the role of lam% for spam filtering evaluation should be re-examined.

References

[1]
G. Cormack, T. Lynam. TREC 2005 Spam Track Overview. TREC 2005. 2005
[2]
J. Goodman, W. Yih. Online Discriminative Spam Filter Training. CEAS 2006. 2006
[3]
G. Cormack. University of Waterloo Participation in the TREC 2007 Spam Track. TREC 2007. 2007.
[4]
T. Joachims. A Support Vector Method for Multivariate Performance Measures. ICML 2005. 2005.
[5]
H.B. Mann and D.R. Whitney. On a Test of Whether One of Two Random Variables Is Stochastically Larger Than the Other. Ann. Math. Statist., 18, 1947.

Cited By

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  • (2021)What is my Problem Identifying Formal Tasks and Metrics in Data Mining on the Basis of Measurement TheoryIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3109823(1-1)Online publication date: 2021
  • (2020)On the nature of information access evaluation metrics: a unifying frameworkInformation Retrieval Journal10.1007/s10791-020-09374-0Online publication date: 29-May-2020
  • (2019)A comparison of filtering evaluation metrics based on formal constraintsInformation Retrieval Journal10.1007/s10791-019-09355-y22:6(581-619)Online publication date: 1-Apr-2019
  • Show More Cited By

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Published In

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2010

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Author Tags

  1. lam
  2. measurement
  3. spam filtering

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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
  • (2021)What is my Problem Identifying Formal Tasks and Metrics in Data Mining on the Basis of Measurement TheoryIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3109823(1-1)Online publication date: 2021
  • (2020)On the nature of information access evaluation metrics: a unifying frameworkInformation Retrieval Journal10.1007/s10791-020-09374-0Online publication date: 29-May-2020
  • (2019)A comparison of filtering evaluation metrics based on formal constraintsInformation Retrieval Journal10.1007/s10791-019-09355-y22:6(581-619)Online publication date: 1-Apr-2019
  • (2011)A comparison of evaluation metrics for document filteringProceedings of the Second international conference on Multilingual and multimodal information access evaluation10.5555/2045274.2045282(38-49)Online publication date: 19-Sep-2011
  • (2011)A Comparison of Evaluation Metrics for Document FilteringMultilingual and Multimodal Information Access Evaluation10.1007/978-3-642-23708-9_6(38-49)Online publication date: 2011

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