Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Publication Details

In: L.McGinty & D. Wilson (eds) International Conference on Case Based Reasoning (ICCBR 2009), LNCS 5650 p.135-149 Springer Verlag

Abstract

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.

DOI

https://doi.org/10.1007/978-3-642-02998-1_11

Funder

Science Foundation Ireland under Grant No. 07/RFP/CMSF718.

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