Skip to main content
SpringerLink
Log in

A prototype-based method for classification with time constraints: a case study on automated planning

  • Theoretical advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

The main goal of Nearest Prototype Classification is to reduce storage space and retrieval time of classical Instance-Based Learning (IBL) algorithms. This motivation is higher in relational data since relational distance metrics are much more expensive to compute than classical distances like Euclidean distance. In this paper, we present an algorithm to build Relational Nearest Prototype Classifiers (RNPCs). When compared with Relational Instance-Based Learning (Relational IBL or RIBL) approaches, the algorithm is able to dramatically reduce the number of instances by selecting the most relevant prototypes, maintaining similar accuracy. The number of prototypes is obtained automatically by the algorithm, although it can also be bound by the user. In this work, we also show an application of RNPC for automated planning. Specifically, we describe a modeling task where a relational policy is built following an IBL approach. This approach uses the decisions taken by a planning system as learning examples. We show that when the number of learning examples is reduced with RNPC, the resulting policy is able to scale up better than the original planning system.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://www.cs.waikato.ac.nz/ml/weka/

  2. http://www.cui.unige.ch/~woznica/rel_weka/

  3. An extensive description of the data sets can be found in: http://www.cui.unige.ch/~woznica/rel_weka/

References

  1. Garain U (2008) Prototype reduction using an artificial immune model. Pattern Anal Appl 11:353–363

    Article  MathSciNet  Google Scholar 

  2. García V, Mollineda RA, Sánchez JS (2008) On the k-NN performance in a challenging scenario of imbalance and overlapping. Pattern Anal Appl 11:269–280

    Article  Google Scholar 

  3. Emde W, Wettschereck D (1996) Relational instance-based learning. In: Proceedings of the 13th international conference on machine learning. Morgan Kaufmann, San Francisco, CA, pp 122–130

  4. Dzeroski S, Lavrac N (2001) Relational data mining. Springer, Berlin

  5. García-Durán R, Fernández F, Borrajo D (2008) Prototypes based relational learning. In: Proceedings of the 13th international conference on artificial intelligence: methodology, systems, applications (AIMSA’08). Lecture Notes in Artificial Intelligence, vol 5253, pp 130–143

  6. Fernández F, Isasi P (2004) Evolutionary design of nearest prototype classifiers. J Heuristics 10(4):431–454

    Article  Google Scholar 

  7. Kuncheva L, Bezdek J (1998) Nearest prototype classification: clustering, genetic algorithms, or random search? IEEE Trans Syst Man Cybern 28(1):160–164

    Article  Google Scholar 

  8. Aha DW, Kibler D, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37–66

    Google Scholar 

  9. Ghallab M, Nau D, Traverso P (2004) Automated planning: theory & practice. Morgan Kaufmann, Amsterdam

    MATH  Google Scholar 

  10. Witten I, Frank E (2005) Data mining. Practical machine learning tools and techniques. Elsevier and Morgan Kaufmann, San Francisco

  11. Kirsten M, Wrobel S, Horváth T (2001) Distance based approaches to relational learning and clustering. In: Dzeroski S, Lavrac N (eds) Relational data mining. Springer, Berlin, pp 213–232

  12. Sebag M (1997) Distance induction in first order logic. In: Džeroski S, Lavrač N (eds) Proceedings of the seventh international workshop on inductive logic programming, vol 1297. Springer, pp 264–272

  13. Ramón J, Bruynooghe M (2001) A polynomial time computable metric between point sets. Acta Inform 37(10):765–780

    Article  MathSciNet  MATH  Google Scholar 

  14. Dzeroski S, Schulze-Kremer S, Heidtke K, Siems K, Wettschereck D (1997) Diterpene structure elucidation from 13C NMR spectra with machine learning. In: Lavrac N, Keravnou E, Zupan B (eds) Intelligent data analysis in Medicine and Pharmacology, Kluwer Academic Publishers, Dordrecht, pp 207–225

  15. Horváth TGT, Alexin Z, Wrobel S (1999) Application of different learning methods to Hungarian part-of-speech tagging. In: Džeroski S, Flach P (eds) ILP99, vol 1634 of LNAI, SV, pp 128–139

  16. Kalousis A, Hilario M (2003) Representational issues in meta-learning, In: Proceedings of the 20th international conference on machine learning, pp 313–320.

  17. Ramón J, Bruynooghe M (2001) A polynomial time computable metric between point sets. Acta Inform 37(10):765–780

    Google Scholar 

  18. Duda RO, Hart PE, Stork DG (2001) Pattern Classification, 2nd edn. Wiley, New York

  19. Woznica A, Kalousis A, Hilario M (2005) Distance-based learning over extended relational algebra structures In: Proceedings of the 15th international conference of inductive logic programming, 2005

  20. Hilario AMM, Kim J, Bradley P, Attwood T (2004) Classifying protein fingerprints. In: Proceedings of the eighth conference on principles and practice of knowledge discovery in databases. Lecture Notes in Computer Science, vol 3202, pp 197–208

  21. Srinivasan A, King R, Muggleton S (1996) The role of background knowledge: using a problem from chemistry to examine the performance of an ILP program, under review for Intelligent Data Analysis in Medicine and Pharmacology. http://citeseer.ist.psu.edu/srinivasan96role.html

  22. Bolckeel H (1998) Top-down induction of first order logical decision trees. Ph.D. Thesis. Department of Computer Science, Katholieke Universiteit Leuven

  23. Leiva H, Atramentov A, Honavar V (2002) Experiments with mrdtl—a multirelational decision tree learning algorithm. In: Proceedings of the workshop on multi-relational decision tree learning

  24. Zimmerman T, Kambhampati S (2003) Learning-assisted automated planning: looking back, taking stock, going forward. AI Mag 24(2):73–96

    Google Scholar 

  25. De la Rosa T, Garcí-Olaya A, Borrajo D (2007) Using cases utility for heuristic planning improvement. In: Case-based reasoning research and development: Proceedings of the 7th international conference on case-based reasoning, vol 4626. Springer, Belfast, Northern Ireland, UK, pp 137–148

  26. Hoffmann J (2003) The metric-ff planning system: translating “ignoring delete lists” to numeric state variables. J Artif Intell Res 20:291–341

    MATH  Google Scholar 

  27. Hoffmann J, Nebel B (2001) The FF planning system: fast plan generation through heuristic search. J Artif Intell Res 14:253–302

    MATH  Google Scholar 

  28. García-Durán R, Fernández F, Borrajo D (2008) Learning and transferring relational instance-based policies. In: Transfer learning for complex task workshop of the AAAI’08. AAAI Press, pp 19–24

Download references

Acknowledgments

This work has been partially supported by the Spanish MICIIN projects TIN2008-06701-C03-03 and TRA2009-0080.

Author information

Authors and Affiliations

  1. Computer Science Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 29011, Leganés, Madrid, Spain

    Rocío García-Durán, Fernando Fernández & Daniel Borrajo

Authors
  1. Rocío García-Durán
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Borrajo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fernando Fernández.

Rights and permissions

Reprints and permissions

About this article

Cite this article

García-Durán, R., Fernández, F. & Borrajo, D. A prototype-based method for classification with time constraints: a case study on automated planning. Pattern Anal Applic 15, 261–277 (2012). https://doi.org/10.1007/s10044-010-0194-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-010-0194-6

Keywords

Search

Navigation