CoAT-APC: When Analogical Proportion-based Classification Meets Case-based Prediction

Fadi Badra (1) , Marie-Jeanne Lesot (2)

1 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé
2 LFI - Learning, Fuzzy and Intelligent systems

This paper proposes to view analogical proportion-based classification as a special type of case-based prediction algorithm, in which (i) cases are differences between two instances, and (ii) only maximally similar cases are compared. It then proposes to tweak the CoAT case-based prediction algorithm in order to implement these two key design principles. The resulting analogical proportion-based classifier CoAT-APC shows a performance comparable to state-of-the-art analogical proportion-based classifiers, while implementing a different transfer strategy, based on the minimization of a dataset complexity measure, as opposed to a rule-based approach. Experimental results show the usefulness of combining these two design principles and suggest that the rule-based transfer strategy of analogical proportionbased classifiers has comparatively little impact on the performance of the system.

Mots clés: analogical proportion-based classification; case-based prediction; interactions between case-based and analogical reasoning

Domaines: Intelligence artificielle [cs.AI]

Fadi Badra, Marie-Jeanne Lesot. CoAT-APC: When Analogical Proportion-based Classification Meets Case-based Prediction. ICCBR Analogies’22: Workshop on Analogies: from Theory to Applications at ICCBR-2022, Sep 2022, Nancy, France. ⟨hal-03767920⟩