Rule Induction

  • Jerzy W. Grzymala-Busse


This chapter begins with a brief discussion of some problems associated with input data. Then different rule types are defined. Three representative rule induction methods: LEM1, LEM2, and AQ are presented. An idea of a classification system, where rule sets are utilized to classify new cases, is introduced. Methods to evaluate an error rate associated with classification of unseen cases using the rule set are described. Finally, some more advanced methods are listed.

Key words

Rule induction algorithms LEM1 LEM2, and AQ LERS Data Mining system LERS classification system rule set types discriminant rule sets validation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Booker L.B., Goldberg D.E., and Holland J.F. Classifier systems and genetic algorithms. In Machine Learning. Paradigms and Methods, Carbonell, J. G. (ed.), The MIT Press, Boston, MA, 1990, 235–282.Google Scholar
  2. Chan C.C. and Grzymala-Busse J.W. On the attribute redundancy and the learning programs ID3, PRISM, and LEM2. Department of Computer Science, University of Kansas, TR-91-14, December 1991, 20 pp.Google Scholar
  3. Dietterich T.G. Machine-learning research. AI Magazine 1997: 97–136.Google Scholar
  4. Grzymala-Busse J.W. Knowledge acquisition under uncertainty—A rough set approach. Journal of Intelligent & Robotic Systems 1988; 1: 3–16.CrossRefMathSciNetGoogle Scholar
  5. Grzymala-Busse J.W. LERS—A system for learning from examples based on rough sets. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, ed. by R. Slowinski, Kluwer Academic Publishers, Dordrecht, Boston, London, 1992, 3–18.Google Scholar
  6. Grzymala-Busse J.W. A new version of the rule induction system LERS, Fundamenta Informaticae 1997; 31: 27–39.zbMATHGoogle Scholar
  7. Holland J.H., Holyoak K.J., and Nisbett R.E. Induction. Processes of Inference, Learning, and Discovery, MIT Press, Boston, MA, 1986.Google Scholar
  8. Japkowicz N. Learning from imbalanced data sets: a comparison of various strategies. Learning from Imbalanced Data Sets, AAAI Workshop at the 17th Conference on AI, AAAI-2000, Austin, TX, July 30–31, 2000, 10–17.Google Scholar
  9. Michalski R.S. A Theory and Methodology of Inductive Learning. In Machine Learning. An Artificial Intelligence Approach, Michalski, R. S., J. G. Carbonell and T. M. Mitchell (eds.), Morgan Kauffman, San Mateo, CA, 1983, 83–134.Google Scholar
  10. Michalski R.S., Mozetic I., Hong J., Lavrac N. The AQ15 inductive learning system: An overview and experiments, Report 1260, Department of Computer Science, University of Illinois at Urbana-Champaign, 1986A.Google Scholar
  11. Michalski R.S., Mozetic I., Hong J., Lavrac N. The multi-purpose incremental learning system AQ 15 and its testing application to three medical domains. Proc. of the 5th Nat. Conf. on AI, 1986B, 1041–1045.Google Scholar
  12. Pawlak Z.: Rough Sets. International Journal of Computer and Information Sciences 1982; 11: 341–356.zbMATHCrossRefMathSciNetGoogle Scholar
  13. Pawlak Z. Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht, Boston, London, 1991.zbMATHGoogle Scholar
  14. Pawlak Z., Grzymala-Busse J.W., Slowinski R. and Ziarko,W. Rough sets. Communications of the ACM 1995; 38: 88–95.CrossRefGoogle Scholar
  15. Rivest R.L. Learning decision lists. Machine Learning 1987; 2: 229–246.MathSciNetGoogle Scholar
  16. Stefanowski J. Algorithms of Decision Rule Induction in Data Mining. Poznan University of Technology Press, Poznan, Poland, 2001.Google Scholar
  17. Weiss S. and Kulikowski C.A. Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, chapter How to Estimate the True Performance of a Learning System, pp. 17–49, San Mateo, CA: Morgan Kaufmann Publishers, Inc., 1991.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Jerzy W. Grzymala-Busse
    • 1
  1. 1.University of KansasLawrenceUSA

Personalised recommendations