INRECA: A Seamlessly Integrated System Based on
Inductive Inference and Case-Based Reasoning

E. Auriol1, S. Wess2, M. Manago1, K.-D. Althoff2, R. Traphner3

1 AcknoSoft, 58a, rue du Dessous-des-Berges, 75013 Paris, France
Phone: +33 1 44248800, Fax: +33 1 44248866, E-mail: {auriol, manago}@ipbc.fr
2 University of Kaiserslautern, Dept. of Computer Science, P0 Box 3049. 67653
Kaiserslautern, Germany. Phone: +49 631 205 3360, Fax: +49 631 205 3357, E-mail:
{althoff,wess}@informatik.uni-kl.de
3 tecInno GmbH, Sauerwiesen 2, 67661 Kaiserslautern, Germany
Phone: +49 6301 60660, Fax: +49 6301 60666

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.
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