Skip to main content
SpringerLink
Log in

Feature Selection for Meta-learning

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

Included in the following conference series:

Abstract

The selection of an appropriate inducer is crucial for performing effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset characteristics and inducer performance to propose inducers for specific datasets. Instance-based learning was applied to meta-learning problems, each one associated with a specific pair of inducers. The generated models were used to provide a ranking of inducers on new datasets.

Instance-based learning assumes that all the attributes have the same importance. We discovered that the best set of discriminating attributes is different for every pair of inducers.We applied a feature selection method on the meta-learning problems, to get the best set of attributes for each problem. The performance of the system is significantly improved.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Agresti, A.: Categorical Data Analysis. Wiley Series in Probability and Mathematical Statistics (1990)

    Google Scholar 

  2. Aha, D.W.: Generalizing from Case Studies: A Case study. In: Proceedings of the 9th International Machine Learning Conference (1992) (1–10)

    Google Scholar 

  3. Brazdil, P., Gama, J., Henery, R.: Characterizing the applicability of classification algorithms using meta level learning. In: proceedings of the European Machine Learning Conference, ECML94, Springer Verlag (1994) 83–102

    Google Scholar 

  4. Brazdil, P. and Carlos, S.: Exploiting past experience in ranking classifiers. In: Proceedings of the 16th International Conference on Machine Learning, Workshop on Recent Advances in meta-Learning and Future Work (1999)

    Google Scholar 

  5. Cohen, W.W.: Fast Effective Rule Induction. In: Proceedings of the 12th International Conference on Machine Learning (1995)

    Google Scholar 

  6. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Willey and sons (1973) 66–73

    Google Scholar 

  7. Gama, J., Brazdil, P.: Characterization of Classification Algorithms. In: Proceedings of the 7th Portugese Conference in AI, EPIA 95 (1995) 83–102

    Google Scholar 

  8. Gama, J., Brazdil, P.: Linear Tree. Intelligent Data Analysis 3 (1999) 1–22

    Article  MATH  Google Scholar 

  9. Hilario, M., Kalousis, A.: Quantifying the resilience of inductive algorithms for classification. In: Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases (2000)

    Google Scholar 

  10. Kalousis, A., Theoharis, T.: NOEMON: Design, implementation and performance results of an intelligent assistant for classifier selection. Intelligent Data Analysis 3(5) (1999) 319–337

    Article  MATH  Google Scholar 

  11. Kalousis, A., Hilario, M.: Knowledge discovery from incomplete data. In: Proceedings of the 2nd International Conference on Data Mining, WIT Press (2000)

    Google Scholar 

  12. Kalousis, A., Hilario, M.: Model Selection via Meta-learning: a Comparative Study. In: Proceedings of the 12th International IEEE Conference on Tools with AI (2000)

    Google Scholar 

  13. Kohavi, R., Sommerfield, D., Dougherty, J.: Data Mining Using MLC++, a Machine Learning Library in C++. Tools With AI (1996)

    Google Scholar 

  14. Kohavi, Ron and John, George Wrappers for Feature Subset Selection Artificial Intelligence Journal 97(1-2) (1997) 273–324

    MATH  Google Scholar 

  15. Lindner, C., Studer, R.: AST: Support for Algorithm Selection with a CBR Approach. In: Proceedings of the 16th International Conference on Machine Learning, Workshop on Recent Advances in Meta-Learning and Future Work (1999)

    Google Scholar 

  16. Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers (1998)

    Google Scholar 

  17. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood Series in Artificial Intelligence (1994)

    Google Scholar 

  18. Nakhacizadeh, G., Schnabl, A.: Development of Multi-Criteria Metrics for Evaluation of Data Mining Algorithms. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (1997) 37–42

    Google Scholar 

  19. Phahringer, B., Bensusan, H., Giraud-Carrier, C.: Tell me who can learn you and I can tell you who you are: Landmarking Various Learning Algorithms. In: Proceedings of the 7th International Conference on Machine Learning Morgan Kaufman (2000) 743–750

    Google Scholar 

  20. Quinlan, R.: http://www.rulequest.com/see5-info.html

  21. Quinlan, R.: Induction of Decision Trees. Machine Learning 1(1) (1986) 81–106

    Google Scholar 

  22. Rendell, L., Seshu, R., Tcheng, D.: Layered Concept Learning and Dynamically Variable Bias Management. In: Proceedings of 10th International Joint Conference on AI (1987) 308–314

    Google Scholar 

  23. Salzberg, L.S.: On Comparing Classifiers: A Critique of Current Research and Methods. Data Mining and Knowledge Discovery 1(3) (1997) 317–327

    Article  Google Scholar 

  24. Schaffer, C.: A Conservation Law for Generalization Performance. In: Proceedings of the 11th International Conference on Machine Learning (1994)

    Google Scholar 

  25. Soares, C., Brazdil, P.,: Zoomed ranking: selection of classification algorithms based on relevant performance information. In: Proceedings of the 4th European Conference on Principles Of Data Mining and Knowledge Discovery. Springer (2000) 126–135

    Google Scholar 

  26. Shavlik, J.W., Mooney, R.J., Towell, G.: Symbolic and Neural net learning algorithms: An experimental approach. Machine Learning 6 (1991) 111–114

    Google Scholar 

  27. Todorovski, L., Dzeroski, S.: Experiments in Meta-Level Learning with ILP In: Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery. Springer (1999) 98–106

    Google Scholar 

  28. Todorovski, L., Brazdil, P., and Soares, C.: Report on the Experiments with Feature Selection in Meta-Level Learning. In: Proceedings of the 4th European Conference on Principles on Data Mining and Knowledge Discovery, Workshop on Data Mining, Decision Support, Meta-learning and ILP (2000) 27–39

    Google Scholar 

  29. Weis, S.M., Kapouleas, I.,: An Empirical Comparison of Patter Recognition, Neural Nets, and Machine Learning Classification Algorithms. In: Proceedings of the 11th International Joint Conference on AI (1989) 781–787

    Google Scholar 

  30. Blake, C., Keogh, E., Merz, C.J.: http://www.ics.uci.edu/~mlearn/MLRepository.html. University of California, Irvine, Dept. of Information and Computer Sciences (1998)

  31. Wolpert, D,: The Lack of A Priori Distinctions Between Learning Algorithms. Neural Computation (1996) 1341–1390

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSD University of Geneva, CH 1211, Geneva 4, Switzerland

    Alexandros Kalousis & Melanie Hilario

Authors
  1. Alexandros Kalousis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melanie Hilario
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kalousis, A., Hilario, M. (2001). Feature Selection for Meta-learning. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_26

Download citation

  • DOI: https://doi.org/10.1007/3-540-45357-1_26

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation