Abstract
This paper focuses on integrating inductive inference and case-based reasoning. We study integration along two dimensions: Integration of case-based methods with methods based on general domain knowledge, and integration of problem solving and incremental learning from experience. In the Inreca system, we perform case-based reasoning as well as tdidt (Top-Down Induction of Decision Trees) classification by using the same data structure called the Inreca tree. We extract decision knowledge using a tdidt algorithm to improve both the similarity assessment by determining optimal weights, and the speed of the overall system by inductive learning. The integrated system we implemented evolves smoothly along application development time from a pure case-based reasoning approach, where each particular case is a piece of knowledge, to a more inductive approach where some subsets of the cases are generalised into abstract knowledge. Our proposed approach is driven by the needs of a concrete pre-commercial system and real diagnostic applications. We evaluate the system on a database of insurance risk for cars and an application involving forestry management in Ireland.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Aamodt, A. (1994). Explanation-Driven Case-Based Reasoning. Richter, Wess et al., 274–288.
Breiman, L., Friedman, J., Olshen, R. & Stone, C. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth.
Cardie, C. (1993). Using decision trees to improve case-based learning. Proc. 10th Int. Conf. on Machine Learning, 25–32.
Friedman, J. H., Bentley, J. L. & Finkel, R. A. (1977). An algorithm for finding best matches in logarithmic expected time. Acm Trans. Math. Software 3, 209–226.
Golding, A. R. & Rosenblum, P. S. (1991). Improving Rule-Based Systems Through Case-Based Reasoning. Proc. AAAI Conference 1991.
Hart, A. (1984). Experience in the use of an inductive system in knowledge engineering. M. Bramer (ed.), Research and Development Systems, Cambridge University Press, 117–126.
Kibler, D. & Aha, D. W. (1987). Learning representative exemplars of concepts: An initial case study. Proc. of the Fourth International Workshop on Machine Learning, pp. 24–30. Irvine, CA: Morgan Kaufmann.
Koopmans, L. H. (1987). Introduction to Contemporary Statistical Methods. Second Edition, Duxbury, Boston.
Manago, M., Althoff, K.-D., Auriol, E., Traphöner, R., Wess, S., Conruyt, N., Maurer, F. (1993). Induction and Reasoning from Cases. Richter, Wess et al., 313–318.
Mikalski, R. & Tecuci, G. (Eds.) (1994). Machine Learning: A Multi-Strategy Approach (Volume IV). San Francisco, CA: Morgan Kaufman.
Mingers, J. (1989). An Empirical Comparison of Selection Measures for Decision-Tree Induction & An Empirical Comparison of Pruning Tree Methods for Decision-Tree Induction. Machine Learning 3 (319–342); 4 (227–242).
Moore, A. W. (1990). Acquisition of dynamic control knowledge for a robotic manipulator. In: Proc. of the Seventh International Conference on Machine Learning, 242–252. Austin, TX: Morgan Kaufman.
Quinlan, R. (1986). Induction of Decision Trees. Machine Learning 1, 81–106.
Quinlan, R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
Richter, M. M., Wess, S., Althoff, K.-D. & Maurer, F. (eds.) (1993). Proc. 1st European Workshop on Case-Based Reasoning (Ewcbr-93).
Salzberg, S. (1991). A Nearest Hyperrectangle Learning Method. Machine Learning 6, 277–309
Sebag, M. & Schoenauer, M. (1994). A Rule-Based Similarity Measure. Richter, Wess et al., 119–131.
Shannon & Weaver (1947). The Mathematical Theory of Computation. University of Illinois Press, Urbana.
Sokal, R. R. & Rahlf, F. J. (1981). Biometry. W. H. Freeman and Co., San Francisco.
Ting, K. M. (1994). The problem of small disjuncts: Its remedy in decision trees. Proc. of the Tenth Canadian Conference on Artificial Intelligence, 91–97.
Utgoff, P. (1988). ID5: An incremental ID3. Fifth International Conference on Machine Learning, Morgan Kaufmann, Los Altos.
Wess, S., Althoff, K.-D. & Derwand, G. (1994). Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning. Wess, Althoff & Richter (Eds.), Topics in Case-Based Reasoning, Springer-Verlag, 167–181.
Wettschereck, D. (1994). A Hybrid Nearest-Neighbor and Nearest-Hyperrectangle Algorithm. Bergadano & De Raedt (Eds.), ECML-94, Springer Verlag, 323–335.
Zhang, J. (1990). A method that combines inductive learning with exemplar-based learning. Proc. for Tools for Artificial Intelligence, 31–37. Herndon, VA: IEEE Computer Society Press.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1995 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Auriol, E., Wess, S., Manago, M., Althoff, K.D., Traphöner, R. (1995). INRECA: A seamlessly integrated system based on inductive inference and case-based reasoning. In: Veloso, M., Aamodt, A. (eds) Case-Based Reasoning Research and Development. ICCBR 1995. Lecture Notes in Computer Science, vol 1010. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60598-3_33
Download citation
DOI: https://doi.org/10.1007/3-540-60598-3_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60598-0
Online ISBN: 978-3-540-48446-2
eBook Packages: Springer Book Archive