WCOID: Maintaining case-based reasoning systems using Weighting, Clustering, Outliers and Internal cases Detection

Abstract

The success of a Case Based Reasoning (CBR) system depends on the quality of case data and the speed of the retrieval process that can be expensive in time especially when the number of cases gets large. To guarantee this quality, maintenance the contents of a case base becomes necessarily. In this paper, we propose a novel case base maintenance (CBM) policy named WCOID - Weighting, Clustering, Outliers and Internal cases Detection, using, in addition to clustering and outliers detection methods, feature weights in the process of improving the competence of our reduced case base. The purpose of our WCOID case base maintenance policy is to reduce both the storage requirements and search time and to focus on balancing case retrieval efficiency and competence for a large size case base. WCOID is mainly based on the idea that a large case base with weighted features is transformed to a small case base.