A Study on Efficient Generation of Decision Trees Using Genetic Programming

For pattern recognition, the 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. On the other hand, NNs are adaptable, and thus have the ability to learn in changing environment. Noting that there is a simple mapping from DT to NN, we can design a DT first, and then map it to an NN. By so doing, we can integrate the symbolic (DT) and the sub-symbolic (NN) approaches, and have advantages of both. For this purpose, we should design DTs which are as small as possible. In this paper, we continue our study on the evolutionary design of the decision trees based on genetic programming, and propose two new methods to reduce the tree sizes. The effectiveness of the new methods are tested through experiments with a character recognition problem.