Skip to main content
SpringerLink
Log in

A Comparison of Rule Induction Using Feature Selection and the LEM2 Algorithm

  • Chapter
  • First Online:
Feature Selection for Data and Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 584))

Abstract

The main objective of this chapter is to compare a strategy of rule induction based on feature selection, exemplified by the LEM1 algorithm, with another strategy, not using feature selection, exemplified by the LEM2 algorithm. The LEM2 algorithm uses all possible attribute-value pairs as the search space. It is shown that LEM2 significantly outperforms LEM1, a strategy based on feature selection in terms of an error rate (5 % significance level, two-tailed test). At the same time, the LEM2 algorithm induces smaller rule sets with the smaller total number of conditions as well. The time complexity for both algorithms is the same.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bazan, J.G., Szczuka, M.S., Wojna, A., Wojnarski, M.: On the evolution of rough set exploration system. In: Proceedings of the Rough Sets and Current Trends in Computing Conference, pp. 592–601 (2004)

    Google Scholar 

  2. Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245–271 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  3. Booker, L.B., Goldberg, D.E., F, H.J.: Classifier systems and genetic algorithms. In: Carbonell, J.G. (ed.) Machine Learning: Paradigms and Methods, pp. 235–282. MIT, Boston (1990)

    Google Scholar 

  4. Chan, C.C., Grzymala-Busse, J.W.: On the attribute redundancy and the learning programs ID3, PRISM, and LEM2. Technical Report, Department of Computer Science, University of Kansas (1991)

    Google Scholar 

  5. Chmielewski, M.R., Grzymala-Busse, J.W.: Global discretization of continuous attributes as preprocessing for machine learning. Int. J. Approx. Reason. 15(4), 319–331 (1996)

    Article  MATH  Google Scholar 

  6. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1, 131–156 (1997)

    Article  Google Scholar 

  7. Everitt, B.: Cluster Analysis. Heinemann Educational Books, London (1980)

    MATH  Google Scholar 

  8. Fang, J., Grzymala-Busse, J.: Leukemia prediction from gene expression data—a  rough set approach. In: Proceedings of the Eighth International Conference on Artificial Intelligence and Soft Computing, pp. 899–908 (2006)

    Google Scholar 

  9. Fang, J., Grzymala-Busse, J.: Mining of microRNA expression data—a rough set approach. In: Proceedings of the First International Conference on Rough Sets and Knowledge Technology, pp. 758–765 (2006)

    Google Scholar 

  10. Fang, J., Grzymala-Busse, J.: Predicting penetration across the blood-brain barrier—a rough set approach. In: Proceedings of the IEEE International Conference on Granular Computing, pp. 231–236 (2007)

    Google Scholar 

  11. Grzymala-Busse, J.W.: LERS—a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht, Boston (1992)

    Chapter  Google Scholar 

  12. Grzymala-Busse, J.W.: A new version of the rule induction system LERS. Fundam. Inform. 31, 27–39 (1997)

    MATH  Google Scholar 

  13. Grzymala-Busse, J.W.: Mining numerical data—a rough set approach. In: Proceedings of the RSEISP’2007, the International Conference of Rough Sets and Emerging Intelligent Systems Paradigms, pp. 12–21 (2007)

    Google Scholar 

  14. Grzymala-Busse, J.W.: Rule induction. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd edn, pp. 249–265. Springer, Berlin (2010)

    Google Scholar 

  15. Grzymala-Busse, J.W.: An empirical comparison of rule induction using feature selection with the LEM2 algorithm. In: Greco, S., Bouchon-Meunier, B.B., Coletti, G., Fedrizzi,  M.M., Matarazzo, B., Yager, R.R. (eds.) Communications in Computer and Information Science, vol. 297, pp. 270–279. Springer (2012)

    Google Scholar 

  16. Grzymala-Busse, J.W., Grzymala-Busse, W.J.: Handling missing attribute values. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd edn, pp. 33–51. Springer, Berlin (2010)

    Google Scholar 

  17. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  18. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction. Foundations and Applications. Springer, Berlin (2006)

    Book  MATH  Google Scholar 

  19. Holland, J.H., Holyoak, K.J., Nisbett, R.E.: Induction: Processes of Inference, Learning, and Discovery. MIT, Boston (1986)

    Google Scholar 

  20. Jain, A., Zongker, D.: Feature selection: evaluation, application, and small sample performance. IEEE Trans. Pattern Anal. Mach. Intell. 19, 153–158 (1997)

    Article  Google Scholar 

  21. Kohavi, R., John, G.: Wrappers for feature selection. Artif. Intell. 97, 273–324 (1997)

    Article  MATH  Google Scholar 

  22. Lei, Y., Huan, L.: Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20-th International Conference on Machine Learning, p. 8 (2003)

    Google Scholar 

  23. Liu, H., Yu, L.: Toward integrating feature selection algorithms for classification and clustering. IEEE Trans. Knowl. Data Eng. 17, 491–502 (2005)

    Article  Google Scholar 

  24. Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman and Hall/CRC, Boca Raton (2007)

    MATH  Google Scholar 

  25. Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In: Proceedings of the National Conference on Artificial Intelligence, pp. 1041–1045. Morgan Kaufmann, San Mateo (1986)

    Google Scholar 

  26. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  27. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)

    Book  MATH  Google Scholar 

  28. Pawlak, Z., Grzymala-Busse, J.W., Slowinski, R., Ziarko, W.: Rough sets. Commun. ACM 38, 89–95 (1995)

    Article  Google Scholar 

  29. Peng, H., Fuhui, L., Chris, D.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226–1238 (2005)

    Article  Google Scholar 

  30. Stefanowski, J.: Algorithms of Decision Rule Induction in Data Mining. Poznan University of Technology Press, Poznan (2001)

    Google Scholar 

  31. Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognit. Lett. 24, 833–849 (2003)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, 66045, USA

    Jerzy W. Grzymała-Busse

  2. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225, Rzeszów, Poland

    Jerzy W. Grzymała-Busse

Authors
  1. Jerzy W. Grzymała-Busse
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jerzy W. Grzymała-Busse .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Urszula Stańczyk

  2. Mawson Lakes Campus, Faculty of Education, Science, Technology and Mathematics, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Grzymała-Busse, J.W. (2015). A Comparison of Rule Induction Using Feature Selection and the LEM2 Algorithm. In: Stańczyk, U., Jain, L. (eds) Feature Selection for Data and Pattern Recognition. Studies in Computational Intelligence, vol 584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45620-0_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-45620-0_8

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45619-4

  • Online ISBN: 978-3-662-45620-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation