Journal of Intelligent Information Systems

, Volume 32, Issue 3, pp 267–295 | Cite as

Gaining insight through case-based explanation

Article

Abstract

Traditional explanation strategies in machine learning have been dominated by rule and decision tree based approaches. Case-based explanations represent an alternative approach which has inherent advantages in terms of transparency and user acceptability. Case-based explanations are based on a strategy of presenting similar past examples in support of and as justification for recommendations made. The traditional approach to such explanations, of simply supplying the nearest neighbour as an explanation, has been found to have shortcomings. Cases should be selected based on their utility in forming useful explanations. However, the relevance of the explanation case may not be clear to the end user as it is retrieved using domain knowledge which they themselves may not have. In this paper the focus is on a knowledge-light approach to case-based explanations that works by selecting cases based on explanation utility and offering insights into the effects of feature-value differences. In this paper we examine to two such a knowledge-light frameworks for case-based explanation. We look at explanation oriented retrieval (EOR) a strategy which explicitly models explanation utility and also at the knowledge-light explanation framework (KLEF) that uses local logistic regression to support case-based explanation.

Keyword

Case-based explanation 

References

  1. Abu-Hanna, A., & de Keizer, N. (2003). Integrating classification trees with local logistic regression in intensive care prognosis. Artificial Intelligence in Medicine, 29, 5–23.CrossRefGoogle Scholar
  2. Andrews, R., Diederich, J., & Tickle, A. (1995). A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Systems, 8, 187–202.CrossRefGoogle Scholar
  3. Armengol, E., Palaudàries, A., & Plaza, E. (2001). Individual prognosis of diabetes long-term risks: A CBR approach. Methods of Information in Medicine, 40, 46–51.Google Scholar
  4. Ashley, K. (1991). Reasoning with cases and hypotheticals in hypo. International Journal of Man–Machine Studies, 34, 753–796.CrossRefGoogle Scholar
  5. Ashley, K., & Aleven, V. (1997). Reasoning symbolically about partially matched cases. In Proceedings of the fifteenth international joint conference on artificial intelligence (IJCAI-97), Nagoya, Japan (pp. 335–341). Morgan Kaufmann: San Francisco.Google Scholar
  6. Ashley, K. D. (1987). Modelling legal argument: Reasoning with cases and hypotheticals. Ph.D. thesis, Department of Computer and Information Science, University of Massachusetts.Google Scholar
  7. Asuncion, A., & Newman, D. (2007). UCI machine learning repository. http://www.ics.uci.edu/~mlearn/MLRepository.htm.
  8. Cheetham, W., & Price, J. (2004). Measures of solution accuracy in case-based reasoning systems. In P. Funk & P. A. G. Calero (Eds.), Advances in case-based reasoning, 7th. European conference on case-based reasoning (ECCBR 2004) (Vol. 3155, pp. 106–118). Lecture Notes in Computer Science. Springer.Google Scholar
  9. Cummins, L., & Bridge, D. (2006). KLEOR: A knowledge lite explanation oriented retrieval. Computing and Informatics, 25(2–3), 173–193.MATHGoogle Scholar
  10. Cunningham, P., Doyle, D., & Loughrey, J. (2003). An evaluation of the usefulness of case-based explanation. In K. D. Ashley & D. G. Bridge (Eds.), Case-based reasoning research and development, 5th international conference on case-based reasoning (ICCBR 2003) (Vol. 2689, pp. 122–130). Lecture Notes in Computer Science. Springer.Google Scholar
  11. Delany, S. J., Cunningham, P., Doyle, D., & Zamolotskikh, A. (2005). Generating estimates of classification confidence for a case-based spam filter. In H. Muñoz-Avila, & F. Ricci (Eds.), Case-based reasoning, research and development, 6th international conference, on case-based reasoning, ICCBR 2005, Chicago, IL, USA, August 23–26, 2005. Proceedings, Lecture notes in Computer Science (Vol. 3620, pp. 177–190). Springer.Google Scholar
  12. Deng, K. (1998). OMEGA: On-line memory-based general purpose system classifier. Ph.D. thesis, The Robotics Institute, School of Computer Science, Carnegie Mellon University.Google Scholar
  13. Doyle, D., Cunningham, P., Bridge, D., & Rahman, Y. (2004). Explanation oreinted retrieval. In P. Funk & P. A. G. Calero (Eds.), Advances in case-based reasoning, 7th. European conference on case-based reasoning (ECCBR 2004) (Vol. 3155, pp. 157–168). Lecture notes in computer science. Springer.Google Scholar
  14. Doyle, D., Cunningham, P., & Walsh, P. (2006). An evaluation of the usefulness of explanation in a cbr system for decision support in bronchiolitis treatment. Computational Intelligence, 22(3-4), 269–281.CrossRefMathSciNetGoogle Scholar
  15. Evans-Romaine, K., & Marling, C. (2003). Prescribing exercise regimens for cardiac and pulmonary disease patients with CBR. In Workshop on CBR in the health sciences at 5th international conference on case-based reasoning (ICCBR-03) (pp. 45–62).Google Scholar
  16. Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95(2), 393–408.CrossRefGoogle Scholar
  17. Hosmer, D., & Lemeshow, S. (2000). Applied logistic regression, 2nd edn. WileyGoogle Scholar
  18. Kass, A., & Leake, D. (1988). Case-based reasoning applied to constructing explanations. In J. Kolodner (Ed.), Proceedings of 1988 workshop on case-based reasoning (pp. 190–208). Morgan Kaufmann.Google Scholar
  19. Kriegsman, M., & Barletta, R. (1993). Building a case-based help desk application. Expert, IEEE [see also IEEE Intelligent Systems and Their Applications], 8(6), 18–26.Google Scholar
  20. Leake, D. (1996a). Case-based reasoning: Experiences, lessons, and future directions, Chapter CBR in context: The present and future (pp. 3–30). AAAI/MIT Press.Google Scholar
  21. Leake, D. (1996b). Case-based reasoning: Experiences, lessons and future directions. AAAI/MIT Press.Google Scholar
  22. Lenz, M., & Burkhard, H. (1996). Case retrieval nets: Basic ideas and extensions. KI-96: Advances in artificial Intelligence, Lecture notes in artificial intelligence, 1137, 227–239.Google Scholar
  23. Majchrzak, A., & Gasser, L. (1991). On using artificial intelligence to integrate the design of organixational and process change in us manufacturing. AI and Society, 5, 321–338.CrossRefGoogle Scholar
  24. McSherry, D. (2003). Explanation in case-based reasoning: An evidential approach. In 8th UK workshop on case-based reasoning (pp. 47–55).Google Scholar
  25. Muñoz-Avila, H., & Ricci, F., (Eds.), Case-based reasoning, research and development, 6th international conference, on case-based reasoning, ICCBR 2005, Chicago, IL, USA, 23–26 August 2005. Proceedings, lecture notes in computer science (Vol. 3620). Springer.Google Scholar
  26. Nottingham, Q. J., Birch, J. B., & Bodt, B. A. (2000). Local logisitic regression an application to army penetration data. Journal of Statistical Computation and Simulation, 66, 35–50.MATHCrossRefMathSciNetGoogle Scholar
  27. Nugent, C., & Cunningham, P. (2005). A case-based explanation system for black-box systems. Artificial Intelligence Review, 24(2), 163–178.MATHCrossRefGoogle Scholar
  28. Nugent, C., Cunningham, P., & Doyle, D. (2005). The best way to instil confidence is by being right. In H. Muñoz-Avila & F. Ricci (Eds.), Case-based reasoning, research and development, 6th international conference, on case-based reasoning, ICCBR 2005, Chicago, IL, USA, 23–26 August 2005. Proceedings, lecture notes in computer science (Vol. 3620, pp. 368–381 (on line 863)). Springer.Google Scholar
  29. Richter, M. (1998). Introduction. In M. Lenz, B. Bartsch-Spörl, H.-D. Burkhard, & S. Wess (Eds.), Case-based reasoning technology, from foundations to applications (pp. 1–16). Springer.Google Scholar
  30. Roth-Berghofer, T. (2004). Explanations and case-based reasoning: Foundational issues. In P. Funk & P. A. G. Calero (Eds.), Advances in case-based reasoning (pp. 389–403). Springer.Google Scholar
  31. Sormo, F., Cassens, J., & Aamodt, A. (2005). Explanation in case-based reasoning perspectives and goals. Artificial Intelligence Review, 24(2), 109–143.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Conor Nugent
    • 1
  • Dónal Doyle
    • 2
  • Pádraig Cunningham
    • 3
  1. 1.4CUniversity College CorkCorkIreland
  2. 2.Idiro Technologies DublinDublinIreland
  3. 3.Computer ScienceUniversity College DublinDublinIreland

Personalised recommendations