Measuring the applicability of self-organization maps in a case-based reasoning system

Abstract

Case-Based Reasoning (CBR) systems solve new problems using others which have been previously resolved. The knowledge is composed of a set of cases stored in a case memory, where each one describes a situation in terms of a set of features. Therefore, the size and organization of the case memory influences in the computational time needed to solve new situations. We organize the memory using Self-Organization Maps, which group cases with similar properties into patterns. Thus, CBR is able to do a selective retrieval using only the cases from the most suitable pattern. However, the data complexity may hinder the identification of patterns and it may degrade the accuracy rate. This work analyses the successful application of this approach by doing a previous data complexity characterization. Relationships between the performance and some measures of class separability and the discriminative power of attributes are also found. © Springer-Verlag Berlin Heidelberg 2007.