Complexity-Guided Case Discovery for Case Based Reasoning

Abstract

The distribution of cases in the case base is critical to the per-formance of a Case Based Reasoning system. The case author is given little support in the positioning of new cases during the development stage of a case base. In this paper we argue that classification boundaries represent important regions of the problem space. They are used to identify locations where new cases should be acquired. We introduce two complexity-guided algorithms which use a local complexity measure and boundary identification techniques to actively discover cases close to boundaries. The ability of these algorithms to dis-cover new cases that significantly improve the accuracy of case bases is demonstrated on five public domain classifica-tion datasets.