Remembering Similitude Terms in CBR

Abstract

In concept learning, inductive techniques perform a global approximation to the target concept. Instead, lazy learning techniques use local approximations to form an implicit global approximation of the target concept. In this paper we present C-LID, a lazy learning technique that uses LID for generating local approximations to the target concept. LID generates local approximations in the form of similitude terms (symbolic descriptions of what is shared by 2 or more cases). C-LID caches and reuses the similitude terms generated in past cases to improve the problem solving of future problems. The outcome of C-LID (and LID) is assessed with experiments on the Toxicology dataset.