Self-Organizing Cases to Find Paradigms

Case-based information systems can be seen as lazy machine learning algorithms; they select a number of training instances and then classify unseen cases as the most similar stored instance. One of the main disadvantages of these systems is the high number of patterns retained. In this paper, a new method for extracting just a small set of paradigms from a set of training examples is presented. Additionally, we provide the set of attributes describing the representative examples that are relevant for classification purposes. Our algorithm computes the Kohonen self-organizing maps attached to the training set to then compute the coverage of each map node. Finally, a heuristic procedure selects both the paradigms and the dimensions (or attributes) to be considered when measuring similarity in future classification tasks.