A study on evolutionary design of binary decision trees

For pattern recognition, decision trees (DTs) are more efficient than neural networks (NNs) for two reasons. First, the computations in making decisions are simpler. Second, important features can be selected automatically during the design process. However, the DTs are not adaptable. This problem can be avoided by mapping a DT to an NN. This mapping not only makes a DT adaptable, but also provides a systematic way for determining the NN structure. In addition, since the features are well selected, the NN obtained from this mapping may have much fewer connections than those designed directly. The key point here is to design a DT which is as small as possible. We study the evolutionary design of the decision trees, and investigate some methods to improve the design efficiency.