Inductive Learning for Case-Based Diagnosis with Multiple Faults

  • Joachim Baumeister
  • Martin Atzmüller
  • Frank Puppe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2416)

Abstract

We present adapted inductive methods for learning similarities, parameter weights and diagnostic profiles for case-based reasoning. All of these methods can be refined incrementally by applying different types of background knowledge. Diagnostic profiles are used for extending the conventional CBR to solve cases with multiple faults. The context of our work is to supplement a medical documentation and consultation system by CBR techniques, and we present an evaluation with a real-world case base.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Michael Richter. The Knowledge contained in Similarity Measures. Invited talk at ICCBR-95, http://www.cbr-web.org/documents/Richtericcbr95remarks.html, 1995.
  2. 2.
    Hans-Peter Buscher, Ch. Engler, A. Führer, S. Kirschke, and F. Puppe. HepatoConsult: A Knowledge-Based Second Opinion and Documentation System. Artificial Intelligence in Medicine, 24(3):205–216, 2002.CrossRefGoogle Scholar
  3. 3.
    Frank Puppe. Knowledge Reuse among Diagnostic Problem-Solving Methods in the Shell-Kit D3. Int. J. Human-Computer Studies, 49:627–649, 1998.CrossRefGoogle Scholar
  4. 4.
    Christoph Beierle and Gabriele Kern-Isberner. Methoden wissensbasierter Systeme. Grundlage, Algorithmen, Anwendungen. Vieweg, 2000.Google Scholar
  5. 5.
    Joachim Baumeister, Dietmar Seipel, and Frank Puppe. Incremental Development of Diagnostic Set-Covering Models with Therapy Effects. In Proc. of the KI-2001 Workshop on Uncertainty in Artificial Intelligence, Vienna, Austria, 2001.Google Scholar
  6. 6.
    Joachim Baumeister and Dietmar Seipel. Diagnostic Reasoning with Multilevel Set—Covering Models. In Proc. of the 13th International Workshop on Principles of Diagnosis (DX-02), Semmering, Austria, 2002.Google Scholar
  7. 7.
    Phyllis Koton. Reasoning about Evidence in Causal Explanations. In Proc. of the Seventh National Conference on Artificial Intelligence, pages 256–261, 1988.Google Scholar
  8. 8.
    Luigi Portinale and Pietro Torasso. ADAPtER: An Integrated Diagnostic System Combining Case-Based and Abductive Reasoning. In Proc. of the ICCBR 1995, pages 277–288, 1995.Google Scholar
  9. 9.
    Cynthia A. Thompson and Raymond J. Mooney. Inductive Learning for Abductive Diagnosis. In Proc. of the AAAI-94, Vol. 1, pages 664–669, 1994.Google Scholar
  10. 10.
    Xue Z. Wang, M.L. Lu, and C. McGreavy. Learning Dynamic Fault Models based on a Fuzzy Set Covering Method. Computers in Chemical Engineering, 21:621–630, 1997.CrossRefGoogle Scholar
  11. 11.
    Rainer Schmidt and Bernhard Pollwein and Lothar Gierl. Case-Based Reasoning for Antibiotics Therapy Advice. In Proc. of the ICCBR 1999, pages 550–559, 1999.Google Scholar
  12. 12.
    James Dougherty, Ron Kohavi, and Mehran Sahami. Supervised and Unsupervised Discretization of Continuous Features. In Proc. of the International Conference on Machine Learning, pages 194–202, 1995.Google Scholar
  13. 13.
    Dan Ventura and Tony R. Martinez. An Empirical Comparison of Discretization Methods. In Proc. of the 10th Int. Symp. on Computer and Information Sciences, pages 443–450, 1995.Google Scholar
  14. 14.
    Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Mateo, California, 2000.Google Scholar
  15. 15.
    Craig Stanfill and David Waltz. Toward Memory-Based Reasoning. Communications of the ACM, 29(12): 1213–1228, 1986.CrossRefGoogle Scholar
  16. 16.
    D. Randall Wilson and Tony R. Martinez. Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research, 6:1–34, 1997.MathSciNetGoogle Scholar
  17. 17.
    Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, San Mateo, California, 1988.Google Scholar
  18. 18.
    Dietrich Wettschereck and David W. Aha. Weighting Features. In Manuela Veloso and Agnar Aamodt, editors, Case-Based Reasoning, Research and Development, First International Conference, pages 347–358, Berlin, 1995. Springer Verlag.Google Scholar
  19. 19.
    Agnar Aamodt and Enric Plaza. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1), 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Joachim Baumeister
    • 1
  • Martin Atzmüller
    • 1
  • Frank Puppe
    • 1
  1. 1.Department of Computer ScienceUniversity of WürzburgWürzburgGermany

Personalised recommendations