SIR: simultaneous induction of rules using neural networks

One major drawback of the decision-tree-based inductive knowledge acquisition methodology is its inability to form high-level features from raw attributes. While neural learning has no such problem, its difficulty is in the opaqueness of the acquired knowledge. The authors address both these issues and present a neural learning methodology that yields production rules formed on the basis of high-level features that are also learned during the learning phase. Furthermore, the competitive component of the learning in the proposed methodology automatically determines the number of rules for a given learning situation. Two examples are presented to illustrate the methodology