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A New Hybrid Method of Generation of Decision Rules Using the Constructive Induction Mechanism

  • Wiesław Paja
  • Krzysztof Pancerz
  • Mariusz Wrzesień
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6401)

Abstract

Our research is devoted to develop a new method of generation of a set of decision rules. This method is compiled using two different mechanisms. The first one is based on applying a new constructive induction algorithm to the investigated dataset. The belief networks are used in this algorithm. The aim is to find the most important descriptive attribute that is calculated on the basis of other attributes. The second part of the presented method constitutes the improvement algorithm that is used in an optimization process of a gathered rule set. The results of our research contain the comparison of classification efficiency using several datasets.

Keywords

decision rule constructive induction belief networks hybrid method classification process 

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References

  1. 1.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Magazine 17, 37–54 (1996)Google Scholar
  2. 2.
    Piramuthu, S., Sikora, R.: Iterative feature construction for improving inductive learning algorithms. Expert Systems with Application 36, 3401–3406 (2009)CrossRefGoogle Scholar
  3. 3.
    Liu, H., Sun, J., Zhang, H.: Post-processing of associative classification rules using closed sets. Expert Systems with Application 36, 6659–6667 (2009)CrossRefGoogle Scholar
  4. 4.
    Spreeuwenberg, S., Gerrits, R.: Requirements for successful verification in practice. In: Haller, S., Simmons, G. (eds.) Proc. of the 15th International Florida Artificial Intelligence Research Society Conference, Pensacola Beach, Florida, USA (2002)Google Scholar
  5. 5.
    Jensen, F.: Logical Foundations for Rule-Based Systems. Springer, Heidelberg (2006)Google Scholar
  6. 6.
    Lo, D., Khoo, S., Wong, L.: Non-redundant sequential rules – theory and algorithm. Information Systems 34(4-5), 438–453 (2009)CrossRefGoogle Scholar
  7. 7.
    Gonzales, A., Barr, V.: Validation and verification of intelligent systems. Journal of Experimental & Theoretical Artificial Intelligence 12(2), 407–420 (2000)Google Scholar
  8. 8.
    Wnek, J., Michalski, R.: Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. Machine Learning 14(2), 139–168 (1994)zbMATHCrossRefGoogle Scholar
  9. 9.
    Friedman, R., Rigel, D., Kopf, A.: Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA: A Cancer Journal for Clinicians 35, 130–151 (1985)CrossRefGoogle Scholar
  10. 10.
    Hippe, Z., Bajcar, S., Blajdo, P., Grzymala-Busse, J., Grzymala-Busse, J., Knap, M., Paja, W., Wrzesien, M.: Diagnosing skin melanoma: Current versus future directions. TASK Quarterly 7(2), 289–293 (2003)Google Scholar
  11. 11.
    Duch, W., Kucharski, T., Gomuła, J., Adamczak, R.: Machine learning methods in analysis of psychometric data. Application to Multiphasic Personality Inventory MMPI-WISKAD, Toruń (1999) (in polish)Google Scholar
  12. 12.
    Hippe, Z.: Machine learning – a promising strategy for business information systems? In: Abramowicz, W. (ed.) Business Information Systems 1997, pp. 603–622. Academy of Economics, Poznan (1997)Google Scholar
  13. 13.
    Jensen, F.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  14. 14.
    Błajdo, P., Grzymała-Busse, J., Hippe, Z., Knap, M., Marek, T., Mroczek, T., Wrzesień, M.: A suite of machine learning programs for data mining: chemical applications. In: Debska, B., Fic, G. (eds.) Information Systems in Chemistry, vol. 2, pp. 7–14. University of Technology Editorial Office, Rzeszow (2004)Google Scholar
  15. 15.
    Paja, W.: RuleSEEKER – a new system to manage knowledge in form of decision rules. In: Tadeusiewicz, R., Ligeza, A., Szymkat, M. (eds.) Computer Methods and Systems. Ed. Office ONT, Cracow, pp. 367–370 (1997) (in polish)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wiesław Paja
    • 1
  • Krzysztof Pancerz
    • 1
  • Mariusz Wrzesień
    • 1
  1. 1.Department of Artificial Intelligence and Expert Systems, Institute of Biomedical InformaticsUniversity of Information Technology and Management in RzeszówPoland

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