Transferring domain rules in a constructive network: introducing RBCC

A new type of neural network is introduced, where symbolic rules are combined using a constructive algorithm. Initially, symbolic rules are converted into networks. Rule-based cascade-correlation (RBCC) then grows its architecture by a competitive process where these rule-based networks strive at capturing as much of the error as possible. A pruning technique for RBCC is also introduced, and the performance of the algorithm is assessed both on a simple artificial problem and on a real-world task of DNA splice-junction determination. Results of the real-world problem demonstrate the advantages of RBCC over other related algorithms in terms of processing time and accuracy.