Early work on case based reasoning reported in the literature shows the importance of case base maintenance for successful practical systems. Different criteria to the maintenance task have been used for more than half a century. In this paper we present different sort out techniques for case base maintenance. All the sort out techniques proposed are based on the same principle: a Rough Sets competence model. First of all, we present sort out reduction techniques based on deletion of cases.

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... , we present sort out techniques that build new reduced competent case memories based on the original ones. The main purpose of these methods is to maintain the competence and reduce, as much as possible, its size. Experiments using different domains, most of them from the UCI repository, show that the reduction techniques maintain the competence obtained by the original case memory. The results are analysed with those obtained using well-known reduction techniques. Rough Sets theory Zdzislaw Pawlak introduced Rough Sets theory in 1982 [9] . The idea of Rough Sets relies on the approximation of a set by a pair of sets. These sets are known as the lower and the upper approximation. These approximations are generated by the available data about the elements of the set. We use Rough Sets theory for extracting the dependencies of knowledge. These dependencies are the basis for computing the relevance of instances into the Case-Based Classifier System. We use two measures of case relevance to decide which cases have to be deleted from the case memory applying different policies. The first measure (Accuracy Rough Sets) captures the degree of completeness of our knowledge. The second one (Class Rough Sets) computes the quality of approximation of each case. The following sections introduce some concepts and definitions required to define how to extract these two measures.