Self-organizing symbolic learned rules

In this paper we present a self-organizing process for rules obtained from a machine learning system. The resulting map can be interpreted back into the symbolic field in an attempt to make the logical representation of the original rubs reflect the relationships codified by map distances. Thus, we improve the quality of the starting set of rules both in classification accuracy and in conceptual clarity.