Using competitive learning between symbolic rules as a knowledge learning method

We present a new knowledge learning method suitable for extracting symbolic rules from domains characterized by continuous domains. It uses the idea of competitive learning, symbolic rule reasoning and it integrates a statistical measure for relevance analysis during the learning process. The knowledge is in form of standard production rules which are available at any time during the learning process. The competition occurs among the rules for capturing a presented instance and the rules can undergo processes of merging, splitting, simplifying and deleting. Reasoning occurs at both higher level of abstraction and lower level of detail. The method is evaluated on publicly available real world datasets. © 2008, IFIP Advances in Information and Communication Technology.All right reserved.