Designing Cost-Sensitive Ensemble – Genetic Approach

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 102)


The paper focuses on the problem of choosing classifiers for a committee of multiple classifier systems. We propose to design such an ensemble on the basis of an executing cost of elementary classifiers and additionally we fix mentioned above cost limit. Properties of the proposed approach were evaluated on the basis of computer experiments which were carried out on varied benchmark datasets. The results of experiments confirm that our proposition can be useful tool for designing cost-sensitive classifier committees.


Cost Limit Access Cost Neural Network Ensemble Pattern Recognition Task Majority Vote Rule 
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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  1. 1.
    Alpaydin, E.: Introduction to Machine Learning, 2nd edn. The MIT Press, London (2010)zbMATHGoogle Scholar
  2. 2.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007),
  3. 3.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and regression trees. Wadsworth and Brooks, Monterey, CA (1984)zbMATHGoogle Scholar
  4. 4.
    Burduk, R.: The new upper bound on the probability of error in binary tree classifier with fuzzy information, pp. 951–961 (2010)Google Scholar
  5. 5.
    Burduk, R.: Costs-Sensitive Classification in Multistage Classifier with Fuzzy Observations of Object Features. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011, Part II. LNCS, vol. 6679, pp. 245–252. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, Hoboken (2001)zbMATHGoogle Scholar
  7. 7.
    Jain, A.K., Duin, P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  8. 8.
    Krzanowski, W., Partrige, D.: Software Diversity: Practical Statistics for its Measurement and Exploatation, Department of Computer Science, University of Exeter (1996)Google Scholar
  9. 9.
    Kuncheva, L.I., Whitaker, C.J.: Ten measures of diversity in classifier ensembles: Limits for two classifiers. In: Proc. of the IEE Workshop on Intelligent Sensor Processing, Birmingham, vol. 10/1–10/6 (2001)Google Scholar
  10. 10.
    Kuncheva, L.I.: Combining pattern classifiers: Methods and algorithms. Wiley Interscience, New Jersey (2004)zbMATHCrossRefGoogle Scholar
  11. 11.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, Hoboken (2001)zbMATHGoogle Scholar
  12. 12.
    Greiner, R., Grove, A., Roth, D.: Learning active classifiers. In: Proceedings of the 13th International Conference on Machine Learning, pp. 207–215 (1996)Google Scholar
  13. 13.
    Hansen, L.K., Salamon, P.: Neural Networks Ensembles. IEEE Trans. on PAMI 12(10), 993–1001 (1990)CrossRefGoogle Scholar
  14. 14.
    Lirov, Y., Yue, O.C.: Automated network troubleshooting knowledge acquisition. Journal of Applied Intelligence 1, 121–132 (1991)CrossRefGoogle Scholar
  15. 15.
    Mitchell, T.M.: Machine Learning. McGraw-Hill Comp., Inc., New York (1997)zbMATHGoogle Scholar
  16. 16.
    Núnez, M.: Economic induction: A case study. In: Proceedings of the Third European Working Session on Learning EWSL1988, pp. 139–145. Morgan Kaufmann, California (1988)Google Scholar
  17. 17.
    Tan, M., Schlimmer, J.: Cost-sensitive concept learning of sensor use in approach and recognition. In: Proceedings of the Sixth International Workshop on Machine Learning ML 1989, Ithaca, New York, pp. 392–395 (1989)Google Scholar
  18. 18.
    Tumer, K., Ghosh, J.: Analysis of Decision Boundaries in Linearly Combined Neural Classifiers. Pattern Recognition 29, 341–348 (1996)CrossRefGoogle Scholar
  19. 19.
    Turney, P.D.: Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. J. Artif. Intell. Res. 2, 369–409 (1995)Google Scholar
  20. 20.
    Verdenius, F.: A method for inductive cost optimization. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 179–191. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  21. 21.
    Wolpert, D.H.: The supervised learning no-free-lunch theorems. In: Proceedings of the 6th Online World Conference on Soft Computing in Industrial Applications (2001)Google Scholar
  22. 22.
    Woźniak, M., Zmyślony, M.: Designing combining classifier with trained fuser - analytical and experimental evaluation. Neural Network World 20(7), 925–934 (2010)Google 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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