Skip to main content
SpringerLink
Log in

Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids

  • Chapter
Data Mining

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cios, K.J., and Liu, N. 1992. Machine learning in generation of a neural network architecture: a Continuous ID3 approach. IEEE Transactions on Neural Networks, 3(2):280–291

    Article  Google Scholar 

  2. Cios, K.J., and Liu, N. 1995. An algorithm which learns multiple covers via integer linear programming. Part I – The CLILP2 Algorithm. Kybernetes, 24: 29–50

    Article  Google Scholar 

  3. Cios, K.J., Wedding, D.K. and Liu, N. 1997. CLIP3: cover learning using integer programming. Kybernetes, 26(4-5):51536

    Article  Google Scholar 

  4. Cios, K.J., Pedrycz, W., and Swiniarski, R. 1998. Data Mining Methods for Knowledge Discovery. Kluwer

    Google Scholar 

  5. Cios, K.J., and Kurgan, L. 2004. CLIP4: Hybrid inductive machine learning algorithm that generates inequality rules. Information Sciences, 163(1–3):37–83

    Article  Google Scholar 

  6. Clark, P., and Niblett, T. 1989. The CN2 algorithm. Machine Learning, 3:261–283

    Google Scholar 

  7. Cohen, W. 1995. Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, 115–123, Lake Tahoe, CA

    Google Scholar 

  8. Cohen, W., and Singer, Y. 1999. A simple, fast and effective rule learner. Proceedings of the 16th National Conference on Artificial Intelligence, 335–342

    Google Scholar 

  9. Han, J., and Kamber, M. 2001. Data Mining: Concepts and Techniques, Morgan Kaufmann

    Google Scholar 

  10. Holte, R.C. 1993. Very simple classification rules perform wellon most commonly used data sets. Machine Learning, 11:63–90

    Article  MATH  Google Scholar 

  11. Hunt, E.B., Marin, J., and Stone, P.J. 1966. Experiments in Induction, Academic Press

    Google Scholar 

  12. Kodratoff, Y. 1988. Introduction to Machine Learning, Morgan-Kaufmann

    Google Scholar 

  13. Kurgan, L., Cios, K.J., and Dick, S. 2006. Highly scalable and robust rule learner: performance evaluation and comparison, IEEE Transactions on Systems Man and Cybernetics, Part B, 36(1):32–53

    Article  Google Scholar 

  14. Langley, P. 1996. Elements of Machine Learning, Morgan-Kaufmann

    Google Scholar 

  15. Michalski, R.S. 1969. On the quasi-minimal solution of the general covering problem. In: Proceedings of the 5th International Symposium on Information Processing (FCIP 69), Bled, Yugoslavia, A3:25–128

    Google Scholar 

  16. Mitchell, T.M. 1997. Machine Learning, McGraw-Hill

    Google Scholar 

  17. Quinlan, J.R. 1993. C4.5 Programs for Machine Learning, Morgan-Kaufmann

    Google Scholar 

  18. Utgoff, P.E. 1989. Incremental induction of decision trees.Machine Learning, 4:161–186

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Dept, Virginia Commonwealth University, Richmond, VA 23284

    Krzysztof J. Cios

  2. University of Colorado, USA

    Krzysztof J. Cios

  3. Computer Science Dept, San Diego State University & Polish Academy of Sciences, San Diego, CA 92182, USA

    Roman W. Swiniarski

  4. Electrical and Computer Engineering Dept, University of Alberta, Edmonton, Alberta T6G 2V4, Canada

    Witold Pedrycz

  5. Electrical and Computer Engineering Dept, University of Alberta, Edmonton, Alberta T6G 2V4, Canada

    Lukasz A. Kurgan

Authors
  1. Krzysztof J. Cios
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roman W. Swiniarski
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Witold Pedrycz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lukasz A. Kurgan
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Cios, K.J., Swiniarski, R.W., Pedrycz, W., Kurgan, L.A. (2007). Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids. In: Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36795-8_12

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-36795-8_12

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-33333-5

  • Online ISBN: 978-0-387-36795-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation