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Multinomial Event Model Based Abstraction for Sequence and Text Classification

  • Dae-Ki Kang
  • Jun Zhang
  • Adrian Silvescu
  • Vasant Honavar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3607)

Abstract

In many machine learning applications that deal with sequences, there is a need for learning algorithms that can effectively utilize the hierarchical grouping of words. We introduce Word Taxonomy guided Naive Bayes Learner for the Multinomial Event Model (WTNBL-MN) that exploits word taxonomy to generate compact classifiers, and Word Taxonomy Learner (WTL) for automated construction of word taxonomy from sequence data. WTNBL-MN is a generalization of the Naive Bayes learner for the Multinomial Event Model for learning classifiers from data using word taxonomy. WTL uses hierarchical agglomerative clustering to cluster words based on the distribution of class labels that co-occur with the words. Our experimental results on protein localization sequences and Reuters text show that the proposed algorithms can generate Naive Bayes classifiers that are more compact and often more accurate than those produced by standard Naive Bayes learner for the Multinomial Model.

Keywords

Class Label Multinomial Model Conditional Probability Table Instance Space Primitive Word 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Pazzani, M.J., Mani, S., Shankle, W.R.: Beyond concise and colorful: Learning intelligible rules. In: Knowledge Discovery and Data Mining, pp. 235–238 (1997)Google Scholar
  2. 2.
    Ashburner, M., Ball, C., Blake, J., Botstein, D., Butler, H., Cherry, J., Davis, A., Dolinski, K., Dwight, S., Eppig, J., Harris, M., Hill, D., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J., Richardson, J., Ringwald, M., Rubin, G., Sherlock, G.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 25, 25–29 (2000)CrossRefGoogle Scholar
  3. 3.
    Undercoffer, J.L., Joshi, A., Finin, T., Pinkston, J.: A Target Centric Ontology for Intrusion Detection: Using DAML+OIL to Classify Intrusive Behaviors. Knowledge Engineering Review (2004)Google Scholar
  4. 4.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American (2001)Google Scholar
  5. 5.
    Kohavi, R., Provost, F.: Applications of data mining to electronic commerce. Data Mining and Knowledge Discovery 5, 5–10 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Zhang, J., Honavar, V.: Learning decision tree classifiers from attribute value taxonomies and partially specified data. In: The Twentieth International Conference on Machine Learning (ICML 2003), Washington, DC (2003)Google Scholar
  7. 7.
    Kang, D.K., Silvescu, A., Zhang, J., Honavar, V.: Generation of attribute value taxonomies from data for data-driven construction of accurate and compact classifiers. In: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), November 1-4, Brighton, UK, pp. 130–137 (2004)Google Scholar
  8. 8.
    Zhang, J., Honavar, V.: AVT-NBL: An algorithm for learning compact and accurate naive bayes classifiers from attribute value taxonomies and data. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Haussler, D.: Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework. Artificial intelligence 36, 177–221 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization (1998)Google Scholar
  11. 11.
    Eyheramendy, S., Lewis, D.D., Madigan, D.: On the naive bayes model for text categorization. In: Ninth International Workshop on Artificial Intelligence and Statistics (2003)Google Scholar
  12. 12.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)zbMATHCrossRefGoogle Scholar
  13. 13.
    Arndt, C.: Information Measures. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  14. 14.
    Slonim, N., Tishby, N.: Agglomerative information bottleneck. In: Proceedings of the 13th Neural Information Processing Systems, NIPS 1999 (1999)Google Scholar
  15. 15.
    Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: CIKM 1998: Proceedings of the seventh international conference on Information and knowledge management, pp. 148–155. ACM Press, New York (1998)CrossRefGoogle Scholar
  16. 16.
    Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  17. 17.
    Apté, C., Damerau, F., Weiss, S.M.: Towards language independent automated learning of text categorization models. In: SIGIR 1994: Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval, New York, NY, USA, pp. 23–30. Springer, Heidelberg (1994)Google Scholar
  18. 18.
    Fawcett, T.: ROC graphs: Notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Labs (2003)Google Scholar
  19. 19.
    Andorf, C., Silvescu, A., Dobbs, D., Honavar, V.: Learning classifiers for assigning protein sequences to gene ontology functional families. In: Fifth International Conference on Knowledge Based Computer Systems (KBCS 2004), pp. 256–265 (2004)Google Scholar
  20. 20.
    Bairoch, A., Apweiler, R.: The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28, 45–48 (2000)CrossRefGoogle Scholar
  21. 21.
    Yan, C., Dobbs, D., Honavar, V.: A two-stage classifier for identification of protein-protein interface residues. In: Proceedings Twelfth International Conference on Intelligent Systems for Molecular Biology / Third European Conference on Computational Biology (ISMB/ECCB 2004), pp. 371–378 (2004)Google Scholar
  22. 22.
    Taylor, M.G., Stoffel, K., Hendler, J.A.: Ontology-based induction of high level classification rules. In: DMKD (1997)Google Scholar
  23. 23.
    Hendler, J., Stoffel, K., Taylor, M.: Advances in high performance knowledge representation. Technical Report CS-TR-3672, University of Maryland Institute for Advanced Computer Studies Dept. of Computer Science (1996)Google Scholar
  24. 24.
    Han, J., Fu, Y.: Exploration of the power of attribute-oriented induction in data mining. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. AIII Press/MIT Press (1996)Google Scholar
  25. 25.
    desJardins, M., Getoor, L., Koller, D.: Using feature hierarchies in bayesian network learning. In: Choueiry, B.Y., Walsh, T. (eds.) SARA 2000. LNCS (LNAI), vol. 1864, pp. 260–270. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  26. 26.
    Gibson, D., Kleinberg, J.M., Raghavan, P.: Clustering categorical data: An approach based on dynamical systems. VLDB Journal: Very Large Data Bases 8, 222–236 (2000)CrossRefGoogle Scholar
  27. 27.
    Ganti, V., Gehrke, J., Ramakrishnan, R.: Cactus - clustering categorical data using summaries. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 73–83. ACM Press, New York (1999)CrossRefGoogle Scholar
  28. 28.
    Pereira, F., Tishby, N., Lee, L.: Distributional clustering of English words. In: 31st Annual Meeting of the ACL, pp. 183–190 (1993)Google Scholar
  29. 29.
    Baker, L.D., McCallum, A.K.: Distributional clustering of words for text classification. In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pp. 96–103. ACM Press, New York (1998)CrossRefGoogle Scholar
  30. 30.
    Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: A new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)Google Scholar
  31. 31.
    Klimt, B., Yang, Y.: The Enron corpus: A new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dae-Ki Kang
    • 1
  • Jun Zhang
    • 1
  • Adrian Silvescu
    • 1
  • Vasant Honavar
    • 1
  1. 1.Artificial Intelligence Research Laboratory, Department of Computer ScienceIowa State UniversityAmesUSA

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