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Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms

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Fuzzy Logic and the Internet

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 137))

Summary

In this contribution, a new Inductive Query by Example process is proposed to automatically derive extended Boolean queries for fuzzy information retrieval systems from a set of relevant documents provided by a user. The novelty of our approach is that it is able to simultanously generate several queries with a different precision-recall tradeoff in a single run. To do so, it is based on an advanced. evolutionary algorithm, GA-P, specially designed to tackle with multiobjective problems by means of a Pareto-based multiobjective technique. The performance of the new proposal will be tested on the usual Cranfield collection and compared to the well-known Kraft et al.’s process.

This work has been supported by CICYT under Project TIC2002–03276 and by the University of Granada under Project “Mejora de Metaheurísticas mediante Hibridación y sus Aplicaciones”.

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References

  1. Bäck T (1996) Evolutionary algorithms in theory and practice. Oxford Univer-sity Press.

    MATH  Google Scholar 

  2. Baeza-Yates R, Ribeiro-Neto, B (1999) Modern information retrieval. Addison-Wesley.

    Google Scholar 

  3. Bordogna G, Carrara P, Pasi G (1995) Fuzzy approaches to extend Boolean information retrieval. In: Bosc P, Kacprzyk J (eds) Fuzziness in database man-agement systems. Physica-Verlag, pp. 231–274.

    Google Scholar 

  4. Chankong V, Haimes Y Y (1983) Multiobjective decision making theory and methodology. North-Holland.

    MATH  Google Scholar 

  5. Chen H, Shankarananrayanan G, She L, Iyer A (1998) Journal of the American Society for Information Science 49(8):693–705.

    Article  Google Scholar 

  6. Coello C A, Van Veldhuizen D A, Lamant G B (2002) Evolutionary algorithms for solving multi-objective problems. Kluwer Academic Publishers.

    MATH  Google Scholar 

  7. Cordon O, Moya F, Zarco C (April, 1999) A brief study on the application of genetic algorithms to information retrieval (in spanish). In: Proc. Fourth In-ternational Society for Knowledge Organization (ISKO) Conference (E000N-SID’99), Granada, Spain, pp. 179–186.

    Google Scholar 

  8. Cordon O, Moya F, Zarco C (September, 1999) Learning queries for a fuzzy information retrieval system by means of GA-P techniques. In: Proc. EUSFLAT-ESTYLF Joint Conference, Palma de Mallorca, Spain, pp. 335–338.

    Google Scholar 

  9. Cordon O, Moya F, Zarco C (2000) Mathware & Soft Computing 7(2–3):309–322.

    MATH  Google Scholar 

  10. Cordon O, Moya F, Zarco C (2002) Soft Computing 6(5):308–319.

    Article  MATH  Google Scholar 

  11. Cordon O, Herrera-Viedma E, Luque M (September, 2002) Evolutionary learn-ing of Boolean queries by multiobjective genetic programming. In: Proc. Seventh Parallel Problem Solving from Nature (PPSN-VII) International Conference, Granada, Spain, LNCS 2439. Springer, pp. 710–719.

    Chapter  Google Scholar 

  12. Cross V (1994) Journal of Intelligent Information Systems 3:29–56.

    Article  Google Scholar 

  13. Deb K, Goldberg D E (1989) An investigation of niche and species formation in genetic function optimization. In: Proc. Third International Conference on Genetic Algorithms (ICGA’89), Hillsdale, USA, pp. 42–50.

    Google Scholar 

  14. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley.

    MATH  Google Scholar 

  15. Eshelman L J, Schaffer J D (1993) Real-coded genetic algorithms and intervalschemata. In: Whitley L D (ed) Foundations of Genetic Algorithms 2, Morgan Kaufmann, pp. 187–202.

    Google Scholar 

  16. Fogel D B (1991) System identification trough simulated evolution. A machine learning approach. Ginn Press, USA.

    Google Scholar 

  17. Fonseca C M, Fleming P J (July, 1993) Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Proc. Fifth International Conference on Genetic Algorithms (ICGA’93), San Mateo, CA, pp. 416–423.

    Google Scholar 

  18. Goldberg D E, Richardson J (1987) Genetic algorithms with sharing for multimodal function optimization. In: Proc. Second International Conference on Genetic Algorithms (ICGA’87), Hillsdale, USA, pp. 41–49.

    Google Scholar 

  19. Gordon M, Pathak P (1999) Information Processing and Management 35(2):141–180.

    Article  Google Scholar 

  20. Herrera-Viedma E (2001) Journal of the American Society for Information Science 52(6):460–475.

    Article  Google Scholar 

  21. Howard L, D’Angelo D (1995) IEEE Expert: 11–15.

    Google Scholar 

  22. Ide E (1971) New experiments in relevance feedback. In: Salton G. (ed) The SMART Retrieval System. Prentice Hall, pp. 337–354.

    Google Scholar 

  23. Koza J (1992) Genetic programming. On the programming of computers by means of natural selection. The MIT Press.

    MATH  Google Scholar 

  24. Kraft D H, Petry F E, Buckles B P, Sadasivan T (1997) Genetic algorithms for query optimization in information retrieval: relevance feedback. In: Sanchez E, Shibata T, Zadeh L A (eds) Genetic algorithms and fuzzy logic systems. World Scientific, pp. 155–173.

    Google Scholar 

  25. Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer-Verlag.

    MATH  Google Scholar 

  26. Mitchel T M (1997) Machine learning. McGraw-Hill.

    Google Scholar 

  27. Rodríguez-Vazquez K, Fonseca C M, Fleming P J (July, 1997) Multiobjective genetic programming: A nonlinear system identification application. In: Late Breaking Papers at the Genetic Programming 1997 Conference, Stanford, USA, pp. 207–212.

    Google Scholar 

  28. Salton G, McGill M J (1989) Introduction to modern information retrieval. McGraw-Hill.

    Google Scholar 

  29. Sanchez E (1989) Information Systems 14(6):455–464.

    Article  Google Scholar 

  30. Sanchez L, Couso I, Corrales J A (2001) Information Sciences 136(1–4):175–191.

    Article  MATH  Google Scholar 

  31. Schaffer J D (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Genetic algorithms and their applications. Proc. of the First International Conference on Genetic Algorithms, pp. 93–100.

    Google Scholar 

  32. Schwefel H-P (1995) Evolution and optimum seeking. Sixth-Generation Computer Technology Series. John Wiley and Sons.

    Google Scholar 

  33. Smith M P, Smith M (1997) Journal of Information Science 23(6):423–431.

    Article  Google Scholar 

  34. van Rijsbergen C J (1979) Information retrieval (2nd edition). Butterworth.

    Google Scholar 

  35. Zadeh L A (1965) Information and Control 8:338–353.

    Article  MathSciNet  MATH  Google Scholar 

  36. Zitzler E, Deb K, Thiele L (2000) Evolutionary Computation 8(2):173–195.

    Article  Google Scholar 

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Cordón, O., Moya, F., Zarco, C. (2004). Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms. In: Loia, V., Nikravesh, M., Zadeh, L.A. (eds) Fuzzy Logic and the Internet. Studies in Fuzziness and Soft Computing, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39988-9_3

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  • DOI: https://doi.org/10.1007/978-3-540-39988-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05770-0

  • Online ISBN: 978-3-540-39988-9

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