Skip to main content
SpringerLink
Log in

A Dynamic Approach to Dimensionality Reduction in Relational Learning

  • Conference paper
  • First Online:
Book cover Foundations of Intelligent Systems (ISMIS 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2366))

Included in the following conference series:

Abstract

We propose the first paradigm that brings Feature Subset Selection to the realm of ILP, in a setting where examples are expressed as non-recursive Datalog Horn clauses. The main idea is to approximate the original relational problem by a multi-instance attribute-value problem, and to perform Feature Subset Selection on that modified representation, suitable for the task. The method acts as a filter: it preprocesses the relational data, prior to model building, and produces relational examples with empirically irrelevant literals removed. An implementation of the paradigm is proposed and successfully applied to the biochemical mutagenesis domain.

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. H. Blockeel, L. de Raedt, N. Jacobs, and B. Demoen. Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery, 3(1):59–93, 1999.

    Article  Google Scholar 

  2. J. Fürnkranz. Dimensionality reduction in ILP: A call to arms. In L. de Raedt and S. Muggleton, editors, Proceedings of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, pages 81–86, August 1997.

    Google Scholar 

  3. E. Alphonse and C. Rouveirol. Lazy propositionalization for relational learning. In W. Horn, editor, Proc. of the 14th European Conference on Artificial Intelligence (ECAI’2000), pages 256–260. IOS Press, 2000.

    Google Scholar 

  4. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1–2):31–71, 1997.

    Article  MATH  Google Scholar 

  5. N. Lavrač, D. Gamberger, and V. Jovanoski. A study of relevance for learning in deductive databases. Journal of Logic Programming, 40(2/3):215–249, 1999.

    Article  MathSciNet  MATH  Google Scholar 

  6. L. de Raedt. Logical settings for concept-learning. Artificial Intelligence, 95(1):187–201, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  7. M. Sebag and C. Rouveirol. Tractable induction and classification in first order logic via stochastic matching. In 15th Int. Join Conf. on Artificial Intelligence (IJCAI’97), pages 888–893. Morgan Kaufmann, 1997.

    Google Scholar 

  8. J.-D. Zucker and J.-G. Ganascia. Changes of representation for efficient learning in structural domains. In Proc. of 13 th International Conference on Machine Learning. Morgan Kaufmann, 1996.

    Google Scholar 

  9. A. Blum and A. Kalai. A note on learning from multiple-instance examples. Machine Learning, 30:23–29, 1998.

    Article  MATH  Google Scholar 

  10. Y. Chevaleyre and J.D. Zucker. Noise-tolerant rule induction for multi-instance data. In ICML 2000, Workshop on Attribute-Value and Relational Learning, 2000.

    Google Scholar 

  11. K. Kira and L. A. Rendell. A practical approach to feature selection. In Proc. of the Ninth Int. Conference on Machine Learning., pages 249–256. MK, 1992.

    Google Scholar 

  12. U. Pompe and I. Kononenko. Linear space induction in first order logic with RELIEFF. In R. Kruse, R. Viertl, and G. Della Ricci, editors, Mathematical and Statistical Methods in Artificial Intelligence, CISM Course and Lecture Notes 363, pages 185–220. Springer-Verlag, 1995.

    Google Scholar 

  13. A. Srinivasan, S. Muggleton, and R.D. King. Comparing the use of background knowledge by inductive logic programming systems. In L. De Raedt, editor, Proc, of the 5th ILP, pages 199–230. Scientific Report, K.U.Leuven, 1995.

    Google Scholar 

  14. F. Esposito, N. Fanizzi, S. Ferilli, and G. Semeraro. Ideal theory refinement under object identity. In Proc. 17th International Conf. on Machine Learning, pages 263–270. Morgan Kaufmann, San Francisco, CA, 2000.

    Google Scholar 

Download references

Author information

Author notes

  1. Stan Matwin

    Present address: University of Ottawa, Canada

Authors and Affiliations

  1. LRI - Bât 490, Université Paris-Sud, 91405, ORSAY CEDEX

    Erick Alphonse & Stan Matwin

Authors
  1. Erick Alphonse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stan Matwin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. UFR d’Informatique, Université Claude Bernard Lyon I, 8, boulevard Niels Bohr, 69622, Villeurbanne Cedex, France

    Mohand-Saïd Hacid

  2. Dept. of Computer Science College of IT, University of North Carolina, Charlotte, NC, 28223, USA

    Zbigniew W. Raś

  3. Băt. L. Equipe de Recherche en Ingénierie des Connaissances, Université Lumière Lyon 2, 5, avenue Pierre Mendes-France, 69676, Bron Cedex, France

    Djamel A. Zighed

  4. LRI, Université Paris Sud, Băt. 490, 91405, Orsay Cedex, France

    Yves Kodratoff

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Alphonse, E., Matwin, S. (2002). A Dynamic Approach to Dimensionality Reduction in Relational Learning. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_29

Download citation

  • DOI: https://doi.org/10.1007/3-540-48050-1_29

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43785-7

  • Online ISBN: 978-3-540-48050-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation