  Although backpropagation ANNs generally predict better than decision trees do
for pattern classification problems, they are often regarded as black boxes,
i.e., their predictions cannot be explained as those of decision trees. In many
applications, it is desirable to extract knowledge from trained ANNs for the
users to gain a better understanding of how the networks solve the problems. A
new rule extraction algorithm, called rule extraction from artificial neural
networks (REANN) is proposed and implemented to extract symbolic rules from
ANNs. A standard three-layer feedforward ANN is the basis of the algorithm. A
four-phase training algorithm is proposed for backpropagation learning.
Explicitness of the extracted rules is supported by comparing them to the
symbolic rules generated by other methods. Extracted 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, such as breast cancer, iris, diabetes, and
season classification problems, demonstrate the effectiveness of the proposed
approach with good generalization ability.
