Classifier Hypothesis Generation Using Visual Analysis Methods

  • Christin Seifert
  • Vedran Sabol
  • Michael Granitzer
Part of the Communications in Computer and Information Science book series (CCIS, volume 87)

Abstract

Classifiers can be used to automatically dispatch the abundance of newly created documents to recipients interested in particular topics. Identification of adequate training examples is essential for classification performance, but it may prove to be a challenging task in large document repositories. We propose a classifier hypothesis generation method relying on automated analysis and information visualisation. In our approach visualisations are used to explore the document sets and to inspect the results of machine learning methods, allowing the user to assess the classifier performance and adapt the classifier by gradually refining the training set.

Keywords

Text Categorisation Visual Analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aha, D.W.: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. Int. J. Man-Mach. Stud. 36(2), 267–287 (1992)CrossRefGoogle Scholar
  2. 2.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)Google Scholar
  3. 3.
    Andrews, K., Kienreich, W., Sabol, V., Becker, J., Droschl, G., Kappe, F., Granitzer, M., Auer, P., Tochtermann, K.: The InfoSky Visual Explorer: Exploiting hierarchical structure and document similarities. Information Visualization 1(3-4), 166–181 (2002)CrossRefGoogle Scholar
  4. 4.
    Axelsson, S.: Combining a bayesian classifier with visualisation: Understanding the IDS. In: Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, pp. 99–108. ACM Press, New York (2004)CrossRefGoogle Scholar
  5. 5.
    Becker, B.G.: Research report: Visualizing decision table classifiers. In: Information Visualization. IEEE Computer Society Press, Los Alamitos (1998)Google Scholar
  6. 6.
    Diri, B., Albayrak, S.: Visualization and analysis of classifiers performance in multi-class medical data. Expert Systems with Applications 34(1), 628–634 (2008)CrossRefGoogle Scholar
  7. 7.
    Guan, H., Zhou, J., Guo, M.: A class-feature-centroid classifier for text categorization. In: Proceedings of the 18th international conference on World Wide Web (WWW), pp. 201–210. ACM, New York (2009)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Keim, D.A., Mansmann, F., Oelke, D., Ziegler, H.: Visual analytics: Combining automated discovery with interactive visualizations. In: Boulicaut, J.-F., Berthold, M.R., Horváth, T. (eds.) DS 2008. LNCS (LNAI), vol. 5255, pp. 2–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Klieber, W., Sabol, V., Muhr, M., Kern, R., Granitzer, M.: Knowledge Discovery using the Knowminer framewor. In: Proceedings of the IADIS International Conference on Information Systems, pp. 307–314 (2009)Google Scholar
  11. 11.
    Krishnan, M., Bohn, S., Cowley, W., Crow, V., Nieplocha, J.: Scalable visual analytics of massive textual datasets. In: IEEE International on Parallel and Distributed Processing Symposium, IPDPS 2007, pp. 1–10 (March 2007)Google Scholar
  12. 12.
    May, T., Kohlhammer, J.: Towards closing the analysis gap: Visual generation of decision supporting schemes from raw data. In: Joint Eurographics and IEEE VGTC Symposium on Visualization (EuroVis), Computer Graphics Forum, vol. 27, pp. 911–918 (2008)Google Scholar
  13. 13.
    Mayer, R., Roiger, A., Rauber, A.: Map-based interfaces for information management in large text collections. Journal of Digital Information Management 6(4), 294–302 (2008)Google Scholar
  14. 14.
    Plaisant, C., Rose, J., Yu, B., Auvil, L., Kirschenbaum, M.G., Smith, M.N., Clement, T., Lord, G.: Exploring erotics in emily dickinson’s correspondence with text mining and visual interfaces. In: JCDL’06: Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, pp. 141–150. ACM, New York (2006)CrossRefGoogle Scholar
  15. 15.
    Poulet, F.: Towards Effective Visual Data Mining with Cooperative Approaches, pp. 389–406. Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Rheingans, P., des Jardins, M.: Visualizing high-dimensional predictive model quality. In: Proceedings of IEEE Visualization, pp. 493–496 (2000)Google Scholar
  17. 17.
    Sabol, V., Kienreich, W., Muhr, M., Klieber, W., Granitzer, M.: Visual knowledge discovery in dynamic enterprise text repositories. In: IV ’09: Proceedings of the 2009 13th International Conference Information Visualisation, pp. 361–368. IEEE Computer Society, Washington (2009)CrossRefGoogle Scholar
  18. 18.
    Seifert, C., Lex, E.: A novel visualization approach for data-mining-related classification. In: Proceedings of the 13th International Conference on Information Visualisation (IV), July 2009, pp. 490–495. Wiley, Chichester (2009)CrossRefGoogle Scholar
  19. 19.
    Seifert, C., Lex, E.: A visualization to investigate and give feedback to classifiers. Poster and Demo at Eurovis 2009 (June 2009) (unpublished)Google Scholar
  20. 20.
    Settles, B.: Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin–Madison (2009)Google Scholar
  21. 21.
    Ware, M., Eibe, F., Holmes, G., Hall, M., Witten, I.H.: Interactive machine learning: letting users build classifiers. International Journal of Human-Computer Studies 55(3), 281–292 (2001)MATHCrossRefGoogle Scholar
  22. 22.
    Wong, P.C., Thomas, J.: Visual analytics. IEEE Computer Graphics and Applications 24, 20–21 (2004)CrossRefGoogle Scholar
  23. 23.
    Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: CIKM ’02: Proceedings of the eleventh international conference on Information and knowledge management, pp. 515–524. ACM Press, New York (2002)CrossRefGoogle Scholar
  24. 24.
    Zhu, X.: Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christin Seifert
    • 1
  • Vedran Sabol
    • 1
  • Michael Granitzer
    • 1
  1. 1.Know-Center GrazAustria

Personalised recommendations