This item is available under a Creative Commons License for non-commercial use only
Case-based approaches to classiﬁcation, as instance-based learning techniques, have a particular reliance on training examples that other supervised learning techniques do not have. In this paper we present the RDCL case proﬁling technique that categorises each case in a case-base based on its classiﬁcation by the case-base, the beneﬁt it has and/or the damage it causes by its inclusion in the case-base. We show how these case proﬁles can identify the cases that should be removed from a case-base in order to improve generalisation accuracy and we show what aspects of existing noise reduction algorithms contribute to good performance and what do not.
Delany, S.: The Good, the Bad and the Incorrectly Classified: Profiling Cases for Case-Base Editing. L.McGinty & D. Wilson (eds) International Conference on Case Based Reasoning (ICCBR 2009), LNCS 5650 p.135-149 Springer Verlag.