Integrating Rules and Cases for the Classification Task



Jerzy Surma
Department of Computer Science
University of Economics
Komandorska 118/120, Wroclaw, Poland
Email: surma@ksk-2.iie.ae.wroc.pl
Koen Vanhoof
Faculty of Applied Economics Science
Limburgs University Center
B-3590 Diepenbeek, Belgium
Email: vanhoof@rsftew.luc.ac.be



Abstract. The recent progress in Case- Based Reasoning has shown that one of the
most important challenges in developing future AL methods will be to combine and
synergistically utilize general and case-based knowledge. In this paper a very
rudimentary kind of integration for the classification task, based on simple
heuristics, is sketched: To solve a problem, first try to use the conventional rule-based 
approach. If it does not work, try to remember a similar problem you have
solved in the past and adapt the old solution to the new situation. This heuristic
approach is based on the knowledge base that consists of rule base and exception
case base. The method of generating this kind of knowledge base from a set of
examples is described. The proposed approach is tested, and compared with
altemative approaches. The experimental results show that the presented integration
method can lead to an improvement in accuracy and comprehensibility.
References

Aamodt, A. (1991). A Knowledge- Intensive, Integrated Approach to Problem Solving and
Sustained Learning. A Doctoral Dissertation - University of Trondheim.
Aamodt, A. (1995). Knowledge Acquisition and Learning by Experience - The Role of Case-Specific 
Knowledge. In Kodratoff, Y., Tecuci, G. (eds.) On Integration of Knowledge
Acquisition and Machine Learning. Academic Press (in press).
Aha, D.W. (1992). Tolerating noisy, irrelevant and novel attributes in instance- based
learning algorithms. International Journal of Man Machine Studies, vol.36, pp.267-287.
Armengol, E., Plaza, E; (1994). Integrating Induction in a Case- Based Reasoner. In Proceed
of the Second European Conference on Case- Based Reasoning. AcknoSoft Press, Paris,
pp.243-252.
Auriol, E., Manago, M., Althoff, K.D., Wess, S., Dittrich,S. (1994). Integration Induction and
Case- Based Reasoning: Methodological Approach and First Evaluation. In Proceed. of the
Second European Conference on Case- Based Reasoning. AcknoSoft Press, Paris, pp.145-
156.
Bamberger, S.K., Goos, K. (1993). Integration of Case- Based Reasoning and Inductive
Learning Methods. In Proceed, of the First European Conference on Case- Based
Reasoning. SEKI Report SR-93-12. University of Kaiserslautern, pp. 296-300.
Biberman, Y. (1995). The Role of Prototypicality in Exemplar-Based Leaming. In Machine
Learning: ECML-95, (Eds.) Lavrac, N., Wrobel, S., Springer Verlag. pp.77-91.
Barletta, R. (1994). A Hybrid Indexing and Retrieval Strategy for Advisory Case- Based
Reasoning Systems Built with ReMind. In Proceed, of the Second European Conference
on Case- Based Reasoning. AcknoSoft Press, Paris, pp.49-58.
Cardie, C. (1993). Using Decision Trees to Improve Case- Based Learning. In Proceed. of the
Tenth International Conference on Machine Learning. Morgan Kaufmann, pp.25-32.
Cost, S., Salzberg, 5. (1993). A Weighted Nearest Neighbor Algorithm for Learning with
Symbolic Features. Machine Learning vol.10, pp.57-78.
Golding, A.R., Rosenbloom, P.S. (1991). Improving Rule- Based System through Case- Based
Reasoning. In Proceed. of the 1991 National Conference on AI The MIT Press, pp.22-27.
Hammond, KJ. (1988). Explaining and Repairing Plans That Fails. Artificial Intelligence
vol.45, pp.173-228.
Kohavi, R., John, G., Long, R., Manley, D., Pflager, K. (1994). MLC++. A Machine Learning
Library in C++. In Tools with Artificial Intelligence Conference.
Koton, P.A. (1988). Reasoning about Evidence in Causal Explanations. In Proceed. AAAI-88,
Morgan Kaufmann, Los Altos, pp.256-261.
Lenz, M. (1993). Cabata - A hybrid Case- Based Reasoning system. In Proceed. of the First
European Conference on Case- Based Reasoning. SEKI Report SR-93-12. University of
Kaiserslautern, pp. 204-209.
Malek, M., Rialle, V. (1994). A Case- Based Reasoning System Applied to Neuropathy
Diagnosis. In Proceed. of the Second European Conference on Case- Based Reasoning.
AcknoSoft Press, Paris, pp.329-336.
Manago, M., Althoff, K.D., Auriol, E., Traphoner, R., Wess, S., Conruyt, N., Maurer, F.
(1993). Induction and Reasoning from Cases. In Proceed. of the first European
Conference on Case- Based Reasoning. SEKI Report SR-93-12. University of
Kaiserslautern, pp. 204-209.
Matwin, S., Plante, B. (1994). Theory Revision by Analyzing Explanations and Prototypes. In
Michalski, R., Tecuci,G. (ads.) Machine Learning vol.4, Morgan Kaufmann, San Mateo.
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo.
Riesbeck, C.K., Schank, R.C. (1989). Inside Case- Based Reasoning. Lawrence Erlbaum,
Hillsdale.
Rissland, E.L., Skalak D.B. (1991). CABARET: rule integration in a hybrid architecture.
International Journal of Man- Machine Studies, vol.34, pp.839-887.
Surma, J. (1994). Enhancing Similarity Measure with Domain Specific Knowledge. In
Proceed. of the Second European Conference on Case- Based Reasoning. AcknoSoft
Press, Paris, pp.365-371.
Utgoff, P.E. (1989). Incremental Decision Tress. Machine Learning, vol.4, pp.161-186.
Zhang, J. (1992). Selecting Typical Instances in Instance- Based Learning. In Proc. of the 9th
Int. Conf. on Machine Learning. Morgan Kaufmann, pp.470479.
