Abstract
Reasoning and learning from cases are based on the concept of similarity often estimated by a distance. This paper presents LID, a learning technique adequate for domains where cases are best represented by relations among entities. LID is able to 1) define a similitude term, a symbolic description of what is shared between a problem and precedent cases; and 2) assess the importance of the relations involved in a similitude term with respect to the purpose of correctly classifying the problem. The paper describes two application domains of relational case-based learning with LID: marine sponges identification and diabetes risk assessment.
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Armengol, E., Plaza, E. (2003). Lazy Induction of Descriptions for Relational Case-Based Learning. In: De Raedt, L., Flach, P. (eds) Machine Learning: ECML 2001. ECML 2001. Lecture Notes in Computer Science(), vol 2167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44795-4_2
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