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A Comparative Study of Catalogue-Based Classification

  • Petra Perner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)

Abstract

In this paper we study the performance of a catalogue-based image classifier after applying different methods for performance improvement, such as feature-subset selection and feature weighting. The performance of the image catalogues is assessed by studying the reduction of the prototypes after applying Chang‘s prototype-selection algorithm. We describe the results that could be achieved and give an outlook for further developments on a catalogue-based classifier.

Keywords

Classification Accuracy Gray Level Feature Subset Respiratory Sinus Arrhythmia Decision Tree Induction 
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.
    Schmidt, R., Gierl, L.: Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications. In: Perner, P. (ed.) MLDM 2001. LNCS (LNAI), vol. 2123, pp. 23–34. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  2. 2.
    Nilsson, M., Funk, P.: A Case-Based Classification of Respiratory Sinus Arrhythmia. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 673–685. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Bichindaritz, I., Kansu, E., Sullivan, K.M.: Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 334–345. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based Learning Algorithm. Machine Learning 6(1), 37–66 (1991)Google Scholar
  5. 5.
    Delany, S.J., Cunningham, P.: An Analysis of Case-Base Editing in a Spam Filtering System. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 128–141. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Ontañón, S., Plaza, E.: Justification-Based Selection of Training Examples for Case Base Reduction. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 310–321. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Hennessy, D.N., Buchanan, B.G., Rosenberg, J.M.: Bayesian Case Reconstruction. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 148–158. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Wettschereck, D., Aha, D.W.: Weighting Features. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 347–358. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  9. 9.
    Perner, P.: CBR-Based Ultra Sonic Image Interpretation. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 479–490. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  10. 10.
    Perner, P., Perner, H., Müller, B.: Mining Knowledge for Hep-3 Cell Image Classification. Artificial Intelligence in Medicine 26, 161–173 (2002)CrossRefGoogle Scholar
  11. 11.
    Chang, C.-L.: Finding Prototypes for Nearest Neighbor Classifiers. IEEE Trans. on Computers C-23(11), 1179–1184Google Scholar
  12. 12.
    Perner, P.: Data Mining on Multimedia Data. LNCS, vol. 2558. Springer, Heidelberg (2002)MATHGoogle Scholar
  13. 13.
    Perner, P.: Improving the Accuracy of Decision Tree Induction by Feature Pre-Selection. Applied Artificial Intelligence 15(8), 747–760Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Petra Perner
    • 1
  1. 1.Institute of Computer Vision and Applied Computer SciencesLeipzig

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