Skip to main content
SpringerLink
Log in

Explaining Probabilistic Fault Diagnosis and Classification Using Case-Based Reasoning

  • Conference paper
Case-Based Reasoning Research and Development (ICCBR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8765))

Included in the following conference series:

Abstract

This paper describes a generic framework for explaining the prediction of a probabilistic classifier using preceding cases. Within the framework, we derive similarity metrics that relate the similarity between two cases to a probability model and propose a novel case-based approach to justifying a classification using the local accuracy of the most similar cases as a confidence measure. As a basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Thereafter, we evaluate the proposed approach for explaining the probabilistic classification of faults by logistic regression. We show that with the proposed approach, it is possible to find cases for which the used classifier accuracy is very low and uncertain, even though the predicted class has high probability.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Wick, M.R., Thompson, W.B.: Reconstructive expert system explanation. Artificial Intelligence 54(1), 33–70 (1992)

    Article  Google Scholar 

  2. Ye, L.R., Johnson, P.E.: The impact of explanation facilities on user acceptance of expert systems advice. Mis Quarterly, 157–172 (1995)

    Google Scholar 

  3. Gregor, S., Benbasat, I.: Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly, 497–530 (1999)

    Google Scholar 

  4. Leake, D., McSherry, D.: Introduction to the special issue on explanation in case-based reasoning. Artificial Intelligence Review 24(2), 103–108 (2005)

    Article  Google Scholar 

  5. Darlington, K.: Aspects of intelligent systems explanation. Universal Journal of Control and Automation 1, 40–51 (2013)

    Article  Google Scholar 

  6. Langlotz, C.P., Shortliffe, E.H.: Adapting a consultation system to critique user plans. International Journal of Man-Machine Studies 19(5), 479–496 (1983)

    Article  Google Scholar 

  7. Olsson, T., Gillblad, D., Funk, P., Xiong, N.: Case-based reasoning for explaining probabilistic machine learning. International Journal of Computer Science & Information Technology (IJCSIT) 6(2) (April 2014)

    Google Scholar 

  8. Isermann, R.: Supervision, fault-detection and fault-diagnosis methods–an introduction. Control Engineering Practice 5(5), 639–652 (1997)

    Article  Google Scholar 

  9. Jayaswal, P., Wadhwani, A., Mulchandani, K.: Machine fault signature analysis. International Journal of Rotating Machinery (2008)

    Google Scholar 

  10. Olsson, E., Funk, P., Xiong, N.: Fault diagnosis in industry using sensor readings and case-based reasoning. Journal of Intelligent and Fuzzy Systems 15(1), 41–46 (2004)

    Google Scholar 

  11. Isermann, R.: Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer (2006)

    Google Scholar 

  12. Olsson, T., Funk, P.: Case-based reasoning combined with statistics for diagnostics and prognosis. Journal of Physics: Conference Series 364(1), 012061 (2012)

    Google Scholar 

  13. Caruana, R., Kangarloo, H., Dionisio, J., Sinha, U., Johnson, D.: Case-based explanation of non-case-based learning methods. In: Proceedings of the AMIA Symposium, p. 212. American Medical Informatics Association (1999)

    Google Scholar 

  14. Nugent, C., Cunningham, P.: A case-based explanation system for black-box systems. Artificial Intelligence Review 24(2), 163–178 (2005)

    Article  MATH  Google Scholar 

  15. Schank, R.C., Leake, D.B.: Creativity and learning in a case-based explainer. Artificial Intelligence 40(1), 353–385 (1989)

    Article  Google Scholar 

  16. Aamodt, A.: Explanation-driven case-based reasoning. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 274–288. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  17. Doyle, D., Tsymbal, A., Cunningham, P.: A review of explanation and explanation in case-based reasoning, vol. 3. Dublin, Trinity college (2003), https://www.cs.tcd.ie/publications/tech-reports/reports

  18. Cunningham, P., Doyle, D., Loughrey, J.: An evaluation of the usefulness of case-based explanation. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 122–130. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  19. McSherry, D.: Explanation in case-based reasoning: an evidential approach. In: Proceedings of the 8th UK Workshop on Case-Based Reasoning, pp. 47–55 (2003)

    Google Scholar 

  20. McSherry, D.: Explaining the pros and cons of conclusions in CBR. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 317–330. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  21. McSherry, D.: A lazy learning approach to explaining case-based reasoning solutions. In: Agudo, B.D., Watson, I. (eds.) ICCBR 2012. LNCS, vol. 7466, pp. 241–254. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  22. Doyle, D., Cunningham, P., Bridge, D., Rahman, Y.: Explanation oriented retrieval. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 157–168. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  23. Cummins, L., Bridge, D.: Kleor: A knowledge lite approach to explanation oriented retrieval. Computing and Informatics 25(2-3), 173–193 (2006)

    MATH  Google Scholar 

  24. Nugent, C.D., Cunningham, P., Doyle, D.: The best way to instil confidence is by being right. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 368–381. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  25. Wall, R., Cunningham, P., Walsh, P.: Explaining predictions from a neural network ensemble one at a time. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 449–460. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  26. Green, M., Ekelund, U., Edenbrandt, L., Björk, J., Hansen, J., Ohlsson, M.: Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients. In: Proceedings of the ICML/UAI/COLT 2008 Workshop on Machine Learning for Health-Care Applications (2008)

    Google Scholar 

  27. Green, M., Ekelund, U., Edenbrandt, L., Björk, J., Forberg, J.L., Ohlsson, M.: Exploring new possibilities for case-based explanation of artificial neural network ensembles. Neural Networks 22(1), 75–81 (2009)

    Article  Google Scholar 

  28. Burkhard, H.D., Richter, M.M.: On the notion of similarity in case based reasoning and fuzzy theory. In: Soft Computing in Case Based Reasoning, pp. 29–45. Springer (2001)

    Google Scholar 

  29. Burkhard, H.D.: Similarity and distance in case based reasoning. Fundamenta Informaticae 47(3), 201–215 (2001)

    MathSciNet  MATH  Google Scholar 

  30. Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  31. Ihara, S.: Information theory for continuous systems, vol. 2. World Scientific (1993)

    Google Scholar 

  32. Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1), 3–55 (2001)

    Article  Google Scholar 

  33. Rachev, S.T., Stoyanov, S.V., Fabozzi, F.J., et al.: A probability metrics approach to financial risk measures. Wiley (2011)

    Google Scholar 

  34. Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory 37(1), 145–151 (1991)

    Article  MATH  Google Scholar 

  35. Cha, S.H.: Comprehensive survey on distance/similarity measures between probability density functions. City 1(2), 1 (2007)

    Google Scholar 

  36. Dragomir, S.C.: Some properties for the exponential of the kullback-leibler divergence. Tamsui Oxford Journal of Mathematical Sciences 24(2), 141–151 (2008)

    MathSciNet  MATH  Google Scholar 

  37. Murphy, K.P.: Machine learning: a probabilistic perspective. MIT Press (2012)

    Google Scholar 

  38. Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In: Advances in Neural Information Processing Systems, vol. 2, pp. 841–848 (2002)

    Google Scholar 

  39. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)

    MATH  Google Scholar 

  40. Steel Plates Faults Data Set. Source: Semeion, Research Center of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy, www.semeion.it , https://archive.ics.uci.edu/ml/datasets/Steel+Plates+Faults (last accessed: May 2014)

  41. Bache, K., Lichman, M.: UCI machine learning repository (2013)

    Google Scholar 

  42. KK-Stiftelse: Swedish Knowledge Foundation, http://www.kks.se (last accessed: September 2013)

Download references

Author information

Authors and Affiliations

  1. School of Innovation, Design, and Engineering, Mälardalen University, Västerås, Sweden

    Tomas Olsson, Peter Funk & Ning Xiong

  2. SICS Swedish ICT, Isafjordsgatan 22, Box 1263, SE-164 29, Kista, Sweden

    Tomas Olsson & Daniel Gillblad

Authors
  1. Tomas Olsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Gillblad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Funk
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ning Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Université Laval, G1K 7P4, Québec, Canada

    Luc Lamontagne

  2. IIIA, Artificial Intelligence Research Institute CSIC, Spanish Council for Scientific Research Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Enric Plaza

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Olsson, T., Gillblad, D., Funk, P., Xiong, N. (2014). Explaining Probabilistic Fault Diagnosis and Classification Using Case-Based Reasoning. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11209-1_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11208-4

  • Online ISBN: 978-3-319-11209-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation