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Improving Similarity Assessment with Entropy-Based Local Weighting

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Book cover Case-Based Reasoning Research and Development (ICCBR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2689))

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Abstract

This paper enhances and analyses the power of local weighted similarity measures. The paper proposes a new entropy-based local weighting algorithm (EBL) to be used in similarity assessment to improve the performance of the CBR retrieval task. We describe a comparative analysis of the performance of unweighted similarity measures, global weighted similarity measures, and local weighting similarity measures. The testing has been done using several similarity measures, and some data sets from the UCI Machine Learning Database Repository and other environmental databases. Main result is that using EBL, and a weight sensitive similarity measure could improve similarity assessment in case retrieval.

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References

  1. D.W. Aha, R.L. Goldstone. Concept learning and flexible weighting. Proceedings of the fourteenth Annual Conference of the Cognitive Science Society. Bloomington, IN. The Cognitive Science Society, Lawrence Erlbaum Associates. 1992.

    Google Scholar 

  2. K. D. Althoff and A. Aamodt. Relating case-based problem solving and learning methods to task and domain characteristics: towards an analytic framework. AI Communications 9(3):109–116, 1996.

    Google Scholar 

  3. C.L. Blake, and C.J. Merz. UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science. 1998.

  4. E. Blanzieri and F. Ricci. Probability Based Metrics for nearest Neighbour classification and Case-Based Reasoning. Procc. of 3 rd International Conference on Case-Based Reasoning, Munich, 1999.

    Google Scholar 

  5. R.H. Creecy, B. M. Masand, S. J. Smith and D. L. Waltz, Trading MIPS and memory for knowledge engineering. Communications of the ACM 35:48–64, 1992.

    Article  Google Scholar 

  6. P. Domingos. Context-sensitive feature selection for lazy learners. Artificial Intelligence Review, 11, 227–253. 1997.

    Article  Google Scholar 

  7. W. Daelemans, A. Van Den Bosch. Generalization performance of backpropagation leraning on to syllabification task. In Proceedings of TWLT3: Connectionism Natural and Language Processing, pp. 27–37. Enschede, The Netherlands. 1992.

    Google Scholar 

  8. J. Dougherty, R. Kohavi and M. Sahami. Supervised and Unsupervised Discretization of continuous Features. Procc. Of the 12 th International Conference on Machine Learning, pp. 194–202, 1995.

    Google Scholar 

  9. N. Fazil. Using Information Gain as Feature Weight. 8th Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN’99), Istanbul, Turkey. 1999.

    Google Scholar 

  10. N. Howe, C. Cardie. Examining locally varying weights for nearest neighbour algorithms. Proceedings of the Second International Conference on Case-Based Reasoning. 1997. pp455–466. Berlin: Springer.

    Google Scholar 

  11. R. Kerber. Chimerge: Discretisation of Numeric Attributes. In Proceedings of 9th Int’l Conference Artificial Intelligence, 1992.

    Google Scholar 

  12. R. Kohavi, P. Langley, and Y. Yun. The utility of feature weighting in nearest-neighbour algorithms. In Proceedings of the European Conference on Machine Learning (ECML97), 1997.

    Google Scholar 

  13. P. Kontkanen, J. Lathinen, P. Myllymäki and H. Tirri.. An unsupervised Bayesian distance measure. Procc. of 5 th Eur. Work.. on Case-based Reasoning (EWCBR’2000). LNAI-1898, pp. 148–160, 2000.

    Google Scholar 

  14. L. Kurgan and K. J. Cios. Discretisation Algorithm that Uses Class-Attribute Interdependence Maximisation, Proceedings of the 2001 International Conference on Artificial Intelligence (IC-AI 2001), pp.980–987, Las Vegas, Nevada.

    Google Scholar 

  15. G.N. Lance and W.T. Williams. Computer Programs for hierarchical polythetic classification (“similarity analyses”), Computer Journal, 9, 60–64, 1966.

    MATH  Google Scholar 

  16. T.W. Liao, and Z. Zhang. Similarity measures for retrieval in case-based reasoning systems, Applied Artificial Intelligence,12,267–288,1998.

    Article  Google Scholar 

  17. T. Mohri and H. Tanaka. An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes, Aha, D. W.,editor, Case-Based Reasoning papers from the 1994 workshop, AAAI Press, Menlo Park, CA.

    Google Scholar 

  18. H. Núñez, M. Sànchez-Marrè and U. Cortés. Similarity Measures in Instance-Based Reasoning. Submitted to Artificial Intelligence, 2003.

    Google Scholar 

  19. H. Núñez, M. Sànchez-Marrè, U. Cortés, J. Comas, I. R-Roda and M. Poch. Feature Weighting Techniques for Prediction tasks in Environmental Processes. Procc. of 3 rd ECAI’2002 Workshop on Binding Environmental Sciences and Artificial Intelligence (BESAI’2002), pp. 4:1–4:9. Lyon, France, 2002.

    Google Scholar 

  20. H.R. Osborne and D. Bridge. Similarity metrics: a formal unification of cardinal and noncardinal similarity measures. Procc. of 2 nd Int. Conf. On Case-based Reasoning (ICCBR’97). LNAI-1266, pp. 235–244, 1997.

    Google Scholar 

  21. H.R. Osborne and D. Bridge. A case-based similarity framework. Procc. of 3 rd Eur. Work.. on Case-based Reasoning (EWCBR’96). LNAI-1168, pp. 309–323, 1996.

    Google Scholar 

  22. F. Ricci and P. Avesani. Learning a local similarity metric for case-based reasoning. In Proceedings of the 1st International Conference on Case-Based Reasoning, Berlin, Springer Verlag pages 301–312, 1995.

    Google Scholar 

  23. M. Sànchez-Marrè, U. Cortés, I. R-Roda, and M. Poch. L’Eixample distance: a new similarity measure for case retrieval. Procc. of 1 st Catalan Conference on Artificial Intelligence (CCIA’98), ACIA bulletin 14-15 pp. 246–253.Tarragona, Catalonia, EU.

    Google Scholar 

  24. R.D. Short and K. Fukunaga. The optimal distance measure for nearest neighbour classification. IEEE transactions on Information Theory. 27:622–627, 1981.

    Article  MATH  MathSciNet  Google Scholar 

  25. C. Stanfill, D. Waltz. Toward Memory-Based Reasoning, Communications of the ACM. 1986.

    Google Scholar 

  26. D. Ventura and T.R. Martinez. And Empirical Comparison of Discretization Methods. Procc. Of the 10 th International Symposium on Computer and Information Sciencies, pp. 443–450, 1995.

    Google Scholar 

  27. D. Wettschereck, D. W. Aha, and T. Mohri. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, Special Issue on lazy learning Algorithms, 1997.

    Google Scholar 

  28. D. Wettschereck and T.G. Dietterich. An experimental comparison of the nearest neighbor and nearest hyperrectangle algorithms. Machine Learning, 19:5–28, 1995.[Wilson and Martinez 1997] D.R. Wilson and T.R. Martínez. Improved Heterogeneous Distance Functions, Journal of Artificial Intelligence Research, 6, 1-34, 1997.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Knowledge Engineering & Machine Learning group, Technical University of Catalonia, Campus Nord-Edifici C5, Jordi Girona 1-3, 08034, Barcelona, Catalonia, EU

    Héctor Núñez, Miquel Sànchez-Marrè & Ulises Cortés

Authors
  1. Héctor Núñez
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  2. Miquel Sànchez-Marrè
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  3. Ulises Cortés
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Editor information

Editors and Affiliations

  1. Learning Research and Development Center, University of Pittsburgh, 3939 O’Hara Street, 15260, Pittsburgh, PA, USA

    Kevin D. Ashley

  2. Department of Computer Science, University College Cork, The Kane Building, College Road, Cork, Ireland

    Derek G. Bridge

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© 2003 Springer-Verlag Berlin Heidelberg

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Núñez, H., Sànchez-Marrè, M., Cortés, U. (2003). Improving Similarity Assessment with Entropy-Based Local Weighting. In: Ashley, K.D., Bridge, D.G. (eds) Case-Based Reasoning Research and Development. ICCBR 2003. Lecture Notes in Computer Science(), vol 2689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45006-8_30

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  • DOI: https://doi.org/10.1007/3-540-45006-8_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40433-0

  • Online ISBN: 978-3-540-45006-1

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