  This paper describes an efficient rule generation algorithm, called rule
generation from artificial neural networks (RGANN) to generate symbolic rules
from ANNs. Classification rules are sought in many areas from automatic
knowledge acquisition to data mining and ANN rule extraction. This is because
classification rules possess some attractive features. They are explicit,
understandable and verifiable by domain experts, and can be modified, extended
and passed on as modular knowledge. A standard three-layer feedforward ANN is
the basis of the algorithm. A four-phase training algorithm is proposed for
backpropagation learning. Comparing them to the symbolic rules generated by
other methods supports explicitness of the generated rules. Generated rules are
comparable with other methods in terms of number of rules, average number of
conditions for a rule, and predictive accuracy. Extensive experimental studies
on several benchmarks classification problems, including breast cancer, wine,
season, golf-playing, and lenses classification demonstrate the effectiveness
of the proposed approach with good generalization ability.
