A comparison of incremental case-based reasoning and inductive learning

Abstract

This paper focuses on problems where the reuse of old solutions seems appropriate but where conventional case-based reasoning (CBR) methodology is not adequate because a complete description of the new problem is not available to trigger case retrieval. We describe an information theoretic technique that solves this problem by producing focused questions to fill out the case description. This use of information theoretic techniques in CBR raises the question of whether a standard inductive learning approach would not solve this problem adequately. The main contribution of this paper is an evaluation of how this incremental case-based reasoning compares with a pure inductive learning approach to the same task.