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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 ofthe 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.
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References
Wick, M.R., Thompson, W.B.: Reconstructive expert system explanation. Artificial Intelligence 54(1), 33–70 (1992)
Ye, L.R., Johnson, P.E.: The impact of explanation facilities on user acceptance of expert systems advice. Mis Quarterly, 157–172 (1995)
Gregor, S., Benbasat, I.: Explanations from intelligent systems: Theoretical foundations and implications for practice. MIS Quarterly, 497–530 (1999)
Leake, D., McSherry, D.: Introduction to the special issue on explanation in case-based reasoning. Artificial Intelligence Review 24(2), 103–108 (2005)
Darlington, K.: Aspects of intelligent systems explanation. Universal Journal of Control and Automation 1, 40–51 (2013)
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)
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)
Isermann, R.: Supervision, fault-detection and fault-diagnosis methods–an introduction. Control Engineering Practice 5(5), 639–652 (1997)
Jayaswal, P., Wadhwani, A., Mulchandani, K.: Machine fault signature analysis. International Journal of Rotating Machinery (2008)
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)
Isermann, R.: Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer (2006)
Olsson, T., Funk, P.: Case-based reasoning combined with statistics for diagnostics and prognosis. Journal of Physics: Conference Series 364(1), 012061 (2012)
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)
Nugent, C., Cunningham, P.: A case-based explanation system for black-box systems. Artificial Intelligence Review 24(2), 163–178 (2005)
Schank, R.C., Leake, D.B.: Creativity and learning in a case-based explainer. Artificial Intelligence 40(1), 353–385 (1989)
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)
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
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)
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)
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)
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)
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)
Cummins, L., Bridge, D.: Kleor: A knowledge lite approach to explanation oriented retrieval. Computing and Informatics 25(2-3), 173–193 (2006)
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)
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)
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)
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)
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)
Burkhard, H.D.: Similarity and distance in case based reasoning. Fundamenta Informaticae 47(3), 201–215 (2001)
Kullback, S., Leibler, R.A.: On information and sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)
Ihara, S.: Information theory for continuous systems, vol. 2. World Scientific (1993)
Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1), 3–55 (2001)
Rachev, S.T., Stoyanov, S.V., Fabozzi, F.J., et al.: A probability metrics approach to financial risk measures. Wiley (2011)
Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory 37(1), 145–151 (1991)
Cha, S.H.: Comprehensive survey on distance/similarity measures between probability density functions. City 1(2), 1 (2007)
Dragomir, S.C.: Some properties for the exponential of the kullback-leibler divergence. Tamsui Oxford Journal of Mathematical Sciences 24(2), 141–151 (2008)
Murphy, K.P.: Machine learning: a probabilistic perspective. MIT Press (2012)
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)
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)
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)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
KK-Stiftelse: Swedish Knowledge Foundation,http://www.kks.se (last accessed: September 2013)
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Authors and Affiliations
School of Innovation, Design, and Engineering, Mälardalen University, Västerås, Sweden
Tomas Olsson, Peter Funk & Ning Xiong
SICS Swedish ICT, Isafjordsgatan 22, Box 1263, SE-164 29, Kista, Sweden
Tomas Olsson & Daniel Gillblad
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- Daniel Gillblad
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- Peter Funk
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- Ning Xiong
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Department of Computer Science and Software Engineering, Université Laval, G1K 7P4, Québec, Canada
Luc Lamontagne
IIIA, Artificial Intelligence Research Institute CSIC, Spanish Council for Scientific Research Campus UAB, 08193, Bellaterra, Catalonia, Spain
Enric Plaza
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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
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