Costs-Sensitive Classification in Multistage Classifier with Fuzzy Observations of Object Features

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6679)


In the paper the problem of cost in hierarchical classifier is presented. Assuming that both the tree structure and the feature used at each non-terminal node have been specified, we present the expected total cost for two cases. The first one concerns the non fuzzy observation of object features, the second concerns the fuzzy observation. At the end of the work the difference between expected total cost of fuzzy and non fuzzy data is determined. Obtained results relate to the locally optimal strategy of Bayes multistage classifier.


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  1. 1.
    Núñez, M.: The use of background knowledge in decision tree induction. Machine Learning 6(3), 231–250 (1991)Google Scholar
  2. 2.
    Penar, W., Woźniak, M.: Experiments on classifiers obtained via decision tree induction methods with different attribute acquisition cost limit. Advances in Soft Computing 45, 371–377 (2007)CrossRefGoogle Scholar
  3. 3.
    Penar, W., Woźniak, M.: Cost-sensitive methods of constructing hierarchical classifiers. Expert Systems 27(3), 146–155 (2010)CrossRefGoogle Scholar
  4. 4.
    Tan, M.: Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning 13, 7–33 (1993)Google Scholar
  5. 5.
    Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and regression trees, California, Wadsworth (1984)Google Scholar
  6. 6.
    Knoll, U., Nakhaeizadeh, G., Tausend, B.: Cost-sensitive pruning of decision trees. In: Proceedings of the Eight European Conference on Machine Learning ECML, vol. 94, pp. 383–386 (1994)Google Scholar
  7. 7.
    Yang, Q., Ling, C., Chai, X., Pan, R.: Test-cost sensitive classification on data with missing values. IEEE Transactions on Knowledge and Data Engineering 18(5), 626–638 (2006)CrossRefGoogle Scholar
  8. 8.
    Saar-Tsechansky, M., Melville, P., Provost, F.: Active feature-value acquisition. Management Science 55(4), 664–684 (2009)CrossRefGoogle Scholar
  9. 9.
    Turney, P.: Cost-sensitive classificcation: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research 2, 369–409 (1995)Google Scholar
  10. 10.
    Burduk, R.: Randomness and fuzziness in Bayes multistage classifier. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010. LNCS (LNAI), vol. 6076, pp. 532–539. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Burduk, R.: Classification error in Bayes multistage recognition task with fuzzy observations. Pattern Analysis and Applications 13(1), 85–91 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kurzyński, M.: On the multistage Bayes classifier. Pattern Recognition 21, 355–365 (1988)CrossRefzbMATHGoogle Scholar
  13. 13.
    Kulkarni, A.: On the mean accuracy of hierarchical classifiers. IEEE Transactions on Computers 27, 771–776 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Kuncheva, L.I.: Combining pattern classifier: Methods and Algorithms. John Wiley, New York (2004)CrossRefzbMATHGoogle Scholar
  15. 15.
    Okuda, T., Tanaka, H., Asai, K.: A formulation of fuzzy decision problems with fuzzy information using probability measures of fuzzy events. Information and Control 38, 135–147 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Zadeh, L.A.: Probability measures of fuzzy events. Journal of Mathematical Analysis and Applications 23, 421–427 (1968)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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