Precision and Recall in Interactive Case-Based Reasoning

Abstract

Often in interactive case-based reasoning (CBR), the case library is irreducible in the sense that the deletion of a single case means that a unique product or fault is no longer represented in the case library. We present empirical measures of precision and recall for irreducible case libraries, identify sources of imperfect precision and recall, and establish an upper bound for the level of precision that can be achieved with any retrieval strategy. Finally, we present a retrieval strategy for irreducible case libraries that gives better precision and recall than inductive retrieval or nearest-neighbour retrieval based on the number of matching features in a target case.