Symbolic interpretation of artificial neural networks

Hybrid Intelligent Systems that combine knowledge-based and artificial neur al network systems typically have four phases involving domain knowledge re presentation. mapping of this knowledge into an initial connectionist archi tecture. network training, and rule extraction, respectively. The final pha se is important because it can provide a trained connectionist architecture with explanation power and validate its output decisions. Moreover, it can be used to refine and maintain the initial knowledge acquired from domain experts. In this paper, we present three rule-extraction techniques. The fi rst technique extracts a set of binary rules from any type of neural networ k. The other two techniques are specific to feedforward networks, with a si ngle hidden layer of sigmoidal units. Technique 2 extracts partial rules th at represent the most important embedded knowledge with an adjustable level of detail, while the third technique provides a more comprehensive and uni versal approach. A rule-evaluation technique, which orders extracted rules based on three performance measures, is then proposed. The three techniques area applied to the iris and breast cancer data sets. The extracted rules are evaluated qualitatively and quantitatively, and are compared with those obtained by other approaches.