This paper describes an efficient algorithm REx for generating symbolic rules
from artificial neural network (ANN). 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. REx exploits
the first order information in the data and finds shortest sufficient
conditions for a rule of a class that can differentiate it from patterns of
other classes. It can generate concise and perfect rules in the sense that the
error rate of the rules is not worse than the inconsistency rate found in the
original data. An important feature of rule extraction algorithm, REx, is its
recursive nature. They are concise, comprehensible, order insensitive and do
not involve any weight values. Extensive experimental studies on several
benchmark classification problems, such as breast cancer, iris, season, and
golf-playing, demonstrate the effectiveness of the proposed approach with good
generalization ability.