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Computer Recognition Systems 2 pp 371–377Cite as

Experiments on Classifiers Obtained Via Decision Tree Induction Methods with Different Attribute Acquisition Cost Limit

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Part of the book series: Advances in Soft Computing ((AINSC,volume 45))

Abstract

The paper presents idea of cost sensitive learning method for decision tree induction with fixed attribute acquisition cost limit. Properties of mentioned concept are established during computer experiments conducted on chosen databases from UCI Machine Learning Repository.

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References

  1. Duda R.O., Hart P.E., Stork D.G.(2001) Pattern Classification, Wiley-Interscience.

    Google Scholar 

  2. Greiner R., Grove A., Roth D.(1996) Learning active classifiers, in: Proceedings of the 13th International Conference on Machine Learning.

    Google Scholar 

  3. Lirov, Y., Yue, O.C.(1991) Automated network troubleshooting knowledge acquisition. Journal of Applied Intelligence, 1:121–132.

    Article  Google Scholar 

  4. Mitchell T.M.(1997) Machine Learning, McGraw-Hill Comp., Inc, New York.

    Google Scholar 

  5. Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/ mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

    Google Scholar 

  6. Norton, S.W.(1989) Generating better decision trees. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89, Detroit, Michigan:800–805.

    Google Scholar 

  7. Núnez, M.(1988) Economic induction: A case study. Proceedings of the Third European Working Session on Learning, EWSL-88, California: Morgan Kaufmann: 139–145.

    Google Scholar 

  8. Nunez, M.(1991) The use of background knowledge in decision tree induction. Machine Learning, 6:231–250.

    Google Scholar 

  9. Quinlan J.R.(1986) Induction on Decision Tree, Machine Learning, Vol. l:81–106

    Google Scholar 

  10. Quinlan J.R., C4.5(1993) Programms for Machine Learning, Morgan Kaufman, San Mateo, CA.

    Google Scholar 

  11. Tan, M., Schlimmer, J.(1989) Cost-sensitive concept learning of sensor use in approach and recognition. Proceedings of the Sixth International Workshop on Machine Learning, ML-89 Ithaca, New York:392-395.

    Google Scholar 

  12. Tan, M., Schlimmer, J.(1990) CSL: A cost-sensitive learning system for sensing and grasping objects. IEEE International Conference on Robotics and Automation. Cincinnati, Ohio.

    Google Scholar 

  13. Tan, M.(1993) Cost-sensitive learning of classification knowledge and its applications in robotics. Machine Learning, 13:7–33.

    Google Scholar 

  14. Verdenius, F.(1991) A method for inductive cost optimization. Proceedings of the Fifth European Working Session on Learning, EWSL-91, New York: SpringerVerlag:179–191.

    Google Scholar 

  15. Turney P.D.(1995) Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, J. Artif. Intell. Res. 2:369–409.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Wojciech Penar

  2. Chair of Systems and Computer Networks, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michal Wozniak

Authors
  1. Wojciech Penar
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  2. Michal Wozniak
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Editor information

Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Marek Kurzynski , Edward Puchala , Michal Wozniak  & Andrzej Zolnierek , ,  & 

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© 2007 Springer-Verlag Berlin Heidelberg

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Penar, W., Wozniak, M. (2007). Experiments on Classifiers Obtained Via Decision Tree Induction Methods with Different Attribute Acquisition Cost Limit. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds) Computer Recognition Systems 2. Advances in Soft Computing, vol 45. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75175-5_47

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  • DOI: https://doi.org/10.1007/978-3-540-75175-5_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75174-8

  • Online ISBN: 978-3-540-75175-5

  • eBook Packages: EngineeringEngineering (R0)

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