Cevm: constrained evidential vocabulary maintenance policy for cbr systems

Abstract

The maintenance of Case-Based Reasoning (CBR) systems has attracted increasing interest within current research since they proved high-quality results in different real-world domains. This kind of systems stores previous experiences, which are described by a vocabulary (e.g., attributes), incrementally in a case base. Actually, the vocabulary presents one among the most important maintenance targets, since it highly contributes in providing accurate solutions and in improving systems’ performance, especially within high-dimensional domains. However, there is no policy, in the literature, that offers the ability to exploit prior knowledge (e.g., given by domain-experts) during the maintenance of features describing cases. In this paper, we propose a flexible policy for the most relevant attribute selection based on the attribute clustering concept. This new policy is able, on the one hand, to manage uncertainty using the belief function theory based tools, and on the other hand, to make use of domain-experts knowledge in form of pairwise constraints: If two attributes offer the same information without any added-value, then a Must-link constraint between them is generated. Otherwise, if there is no relation between them and they offer different information, then a Cannot-link constraint between them is created.