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Expert-Assisted Classification Rules Extraction Algorithm

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Advances in Databases and Information Systems (ADBIS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6295))

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Abstract

Machine learning algorithms nowadays are important and well-accepted tools which help in demanding and ever-more challenging data analysis in many fields. In this paper, we study an approach to machine learning and knowledge discovery, where a learning algorithm uses experts’ domain knowledge to induce solutions, and experts use the algorithm and its solutions to enhance their “information processing strength”. An adaptation of evolutionary method AREX for automatic extraction of rules is presented that is based on the evolutionary induction of decision trees and automatic programming. The method is evaluated in a case study on a medical dataset. The obtained results are assessed to evaluate the strength and potential of the proposed classification rules extraction algorithm.

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References

  1. Anderson, H.R., et al.: Clinicians Illustrated Dictionary of Cardiology. Science Press, London (1991)

    Google Scholar 

  2. Anglano, C., Giordana, A., Lo Bello, G., Saitta, L.: An experimental evaluation of coevolutive concept learning. In: Proceedings of the International Conference on Machine Learning ICML 1998, pp. 19–27 (1998)

    Google Scholar 

  3. DeJong, K.A.: Hybrid learning using genetic algorithms and decision trees for pattern classification. In: Proceedings of the IJCAI Conference (1995)

    Google Scholar 

  4. Freitas, A.A.: A survey of evolutionary algorithms for data mining and knowledge discovery. In: Advances in Evolutionary Computation, pp. 819–845. Springer, Heidelberg (2002)

    Google Scholar 

  5. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  6. Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in Medicine 23, 89–109 (2001)

    Article  Google Scholar 

  7. Magoulas, G.D., Prentza, A.: Machine Learning in Medical Applications. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds.) ACAI 1999. LNCS (LNAI), vol. 2049, pp. 300–307. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  8. Machine Learning in C++, MLC++ library, http://www.sgi.com/tech/mlc

  9. Papagelis, A., Kalles, D.: Breeding decision trees using evolutionary techniques. In: Proceedings of the ICML 2001 (2001)

    Google Scholar 

  10. Peña-Reyes, C.A., Sipper, M.: Evolutionary computation in medicine: an overview. Artificial Intelligence in Medicine 19(1), 1–23 (2000)

    Article  Google Scholar 

  11. Podgorelec, V.: ProGenesys – program generation tool based on genetic systems. In: Proceedings of the ICAI 1999, pp. 299–302 (1999)

    Google Scholar 

  12. Podgorelec, V., Kokol, P.: Towards more optimal medical diagnosing with evolutionary algorithms. Journal of Medical Systems 25(3), 195–220 (2001)

    Article  Google Scholar 

  13. Podgorelec, V., Kokol, P.: Evolutionary induced decision trees for dangerous software modules prediction. Information Processing Letters 82(1), 31–38 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  14. Podgorelec, V., Kokol, P., Molan Stiglic, M., Hericko, M., Rozman, I.: Knowledge discovery with classification rules in a cardiovascular dataset. Computer Methods and Programs in Biomedicine 80(1), S39–S49 (2005)

    Article  Google Scholar 

  15. Podgorelec, V., Zorman, M.: Decision Trees. In: Meyers, R.A. (ed.) Encyclopedia of Complexity and Systems Science, vol. 2, pp. 1826–1845. Springer, New York (2009)

    Google Scholar 

  16. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  17. RuleQuest Research Data Mining Tools, http://www.rulequest.com

  18. Ryan, C.: Shades – a polygenic inheritance scheme. In: Proceedings of Mendel 1997 Conference, pp. 140–147 (1997)

    Google Scholar 

  19. Sack, U., Knoechner, S., Warschkau, H., Pigla, U., Emmrich, F., Kamprada, M.: Computer-assisted classification of HEp-2 immunofluorescence patterns in autoimmune diagnostics. Autoimmunity Reviews 2, 298–304 (2003)

    Article  Google Scholar 

  20. Tan, K.C., Yu, Q., Heng, C.M., Lee, T.H.: Evolutionary computing for knowledge discovery in medical diagnosis. Artificial Intelligence in Medicine 27(2), 129–154 (2003)

    Article  Google Scholar 

  21. Wang, X.-H., Zheng, B., Good, W.F., King, J.L., Chang, Y.-H.: Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network. International Journal of Medical Informatics 54, 115–126 (1999)

    Article  Google Scholar 

  22. Weiss, S.M., Indurkhya, N.: Predictive Data Mining. Morgan Kaufmann, San Francisco (1998)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Maribor, FERI, Smetanova ulica 17, SI-2000, Maribor, Slovenia

    Vili Podgorelec

Authors
  1. Vili Podgorelec
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Editor information

Editors and Affiliations

  1. Department of Computer and Infoamtion Science, University of Genova, Via Dodecaneso, 35, 16146, Genova, Italy

    Barbara Catania

  2. Faculty of Science, Department of Mathematics and Informatics, University of Novi Sad, Trg Dositeja Obradovica 4, 21000, Novi Sad, Serbia

    Mirjana Ivanović

  3. Institute of Computer Science and Applied Mathematics, Christian-Albrechts-University of Kiel, Olshausenstr. 40, 24098, Kiel, Germany

    Bernhard Thalheim

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Podgorelec, V. (2010). Expert-Assisted Classification Rules Extraction Algorithm. In: Catania, B., Ivanović, M., Thalheim, B. (eds) Advances in Databases and Information Systems. ADBIS 2010. Lecture Notes in Computer Science, vol 6295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15576-5_34

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  • DOI: https://doi.org/10.1007/978-3-642-15576-5_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15575-8

  • Online ISBN: 978-3-642-15576-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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