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Transactions on Rough Sets XVIII pp 69–91Cite as

Three-Way Decisions Versus Two-Way Decisions on Filtering Spam Email

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Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8449))

Abstract

A three-way decisions solution and a two-way decisions solution for filtering spam emails are examined in this paper. Compared to two-way decisions, the spam filtering is no longer viewed as a binary classification problem, and each incoming email is accepted as a legitimate or rejected as a spam or undecided as a further-examined email in the three-way decisions. One advantage of the three-way decisions solution for spam filtering is that it can reduce the error rate of classifying a legitimate email to spam with minimum misclassification cost. The other one is that the solution can provide a more meaningful decision procedure for users while it is not restricted to a specific classifier. Experimental results on several corpus show that the three-way decisions solution can get a lower error rate and a lower misclassification cost.

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Notes

  1. 1.

    All corpus are available from http://labs-repos.iit.demokritos.gr/skel/i-config/.

References

  1. Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C.D., Stamatopoulos, P.: Learning to filter spam e-mail: a comparison of a naive bayesian and a memory-based approach. In: 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 1–13 (2000)

    Google Scholar 

  2. Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: 7th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (2010)

    Google Scholar 

  3. Carreras, X., Marquez, L.: Boosting trees for anti-spam email filtering. In: European Conference on Recent Advances in NLP (2001)

    Google Scholar 

  4. Chow, C.K.: On optimum recognition error and reject tradeoff. IEEE Trans. Inf. Theory 16(1), 41–46 (1970)

    Article  Google Scholar 

  5. DeBarr, D., Wechsler, H.: Spam detection using clustering, random forests, and active learning. In: 6th Conference on Email and Anti-Spam (2009)

    Google Scholar 

  6. Domingos, P., Pazzani, M.: Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: 13th International Conference on Machine Learning, pp. 105–112 (1996)

    Google Scholar 

  7. Drucker, H., Wu, D.H., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–1054 (1999)

    Article  Google Scholar 

  8. Elkan, C.: The foundations of cost-sensitive learning. In: 17th International Joint Conference on Artificial Intelligence, pp. 973–978 (2001)

    Google Scholar 

  9. Fiumara, G., Marchi, M., Pagano, R., Provetti, A., Spada, N.: A rule-based system for end-user e-mail annotations. In: 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, pp. 102–108 (2011)

    Google Scholar 

  10. Halder, S., Wei, C., Sprague, A.: Overview of existing methods of spam mining and potential usefulness of sender’s name as parameter for fuzzy string matching. ACEEE Int. J. Inf. Techonol. 1(1), 25–28 (2011)

    Google Scholar 

  11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 130–133 (2009)

    Article  Google Scholar 

  12. Khonji, M., Jones, A., Iraqi, Y.: A study of feature subset evaluators and feature subset searching methods for phishing classification. In: 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Coference, pp. 135–144 (2011)

    Google Scholar 

  13. Khorsi, A.: An overview of content-based spam filtering techniques. Informatica 31, 269–277 (2007)

    MATH  Google Scholar 

  14. Metsis, V., Androutsopoulos, I., Paliouras, G.: Spam filtering with naive bayes-which naive bayes? In: 3rd Conference on Email and Anti-Spam (2006)

    Google Scholar 

  15. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)

    MATH  Google Scholar 

  16. Pauker, S.G., Kassirer, J.P.: The threshold approach to clinical decision making. N. Engl. J. Med. 302, 1109–1117 (1980)

    Article  Google Scholar 

  17. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  Google Scholar 

  18. Perez-Diaz, N., Ruano-Ordas, D., Mendez, J.R., Galvez, J.F., Fdez-Riverola, F.: Rough sets for spam filtering: selecting appropriate decision rules for boundary e-mail classification. Appl. Soft Comput. 12(11), 3671–3682 (2012)

    Article  Google Scholar 

  19. Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. In: Learning for Text Categorization-Papers from the AAAI Workshop, pp. 55–62 (1996)

    Google Scholar 

  20. Schneider, K.M.: A comparison of event models for Naive Bayes anti-spam e-mail filtering. In: 10th Conference of the European Chapter of the Association for Computational Linguistics, pp. 307–314 (2003)

    Google Scholar 

  21. Wei, C., Sprague, A., Warner, G., Skjellum, A.: Identifying new spam domains by hosting IPs: improving domain blacklisting. In: 7th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Coference (2010)

    Google Scholar 

  22. Yao, Y.Y., Wong, S.K.M., Lingras, P.: A decision-theoretic rough set model. Methodol. Intell. Syst. 5, 17–24 (1992)

    Google Scholar 

  23. Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180, 341–353 (2010)

    Article  MathSciNet  Google Scholar 

  24. Zhao, W., Zhu, Y.: An email classification scheme based on decision-theoretic rough set theory and analysis of email security. In: 2005 IEEE Region 10 TENCON, pp. 1–C6 (2005)

    Google Scholar 

  25. Zhou, B., Yao, Y.Y., Luo, J.G.: A three-way decision approach to email spam filtering. In: 23th Canadian Conference on Artificial Intelligence, pp. 28–39 (2010)

    Chapter  Google Scholar 

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Acknowledgments

This research is supported by the National Natural Science Foundation of China under Grant No. 61170180, and the China Postdoctoral Science Foundation under Grant No. 2013M530259, and Postdoctoral Science Foundation of Jiangsu Province under Grant No. 1202021C and Natural Science Foundation of Jiangsu Province under Grant No. BK20140800.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiuyi Jia

  2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Xiuyi Jia & Lin Shang

Authors
  1. Xiuyi Jia
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  2. Lin Shang
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Corresponding author

Correspondence to Xiuyi Jia .

Editor information

Editors and Affiliations

  1. ECE Department Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB, Canada

    James F. Peters

  2. University of Warsaw, Warsaw, Poland

    Andrzej Skowron  & Hung Son Nguyen  & 

  3. Southwest Jiaotong University, Sichuan, China

    Tianrui Li  & Yan Yang  & 

  4. University of Regina, Regina, SK, Canada

    JingTao Yao

Experimental Result Tables

Experimental Result Tables

Table A1. Measure group 1 results on corpora Ling-Spam-bare.
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Table A2. Measure group 1 results on corpora Ling-Spam-lemm.
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Table A3. Measure group 1 results on corpora Ling-Spam-stop.
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Table A4. Measure group 1 results on corpora PU1
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Table A5. Measure group 1 results on corpora PU2.
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Table A6. Measure group 1 results on corpora PU3.
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Table A7. Measure group 1 results on corpora PUA.
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Table A8. Measure group 1 results on corpora Enron-Spam.
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Table A9. Four group of cost functions used in the experiment
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Table A10. Accuracy and error rate on corpora Ling-Spam-stop.
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Table A11. Accuracy and error rate on corpora PU1.
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Table A12. Accuracy and error rate on corpora PU2.
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Table A13. Accuracy and error rate on corpora PUA.
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Table A14. Misclassification cost on corpora Ling-Spam-stop.
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Table A15. Misclassification cost on corpora PU1.
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Table A16. Misclassification cost on corpora PU2.
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Table A17. Misclassification cost on corpora PUA.
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Jia, X., Shang, L. (2014). Three-Way Decisions Versus Two-Way Decisions on Filtering Spam Email. In: Peters, J.F., Skowron, A., Li, T., Yang, Y., Yao, J., Nguyen, H.S. (eds) Transactions on Rough Sets XVIII. Lecture Notes in Computer Science(), vol 8449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44680-5_5

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  • DOI: https://doi.org/10.1007/978-3-662-44680-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44679-9

  • Online ISBN: 978-3-662-44680-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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