A computational study of using genetic algorithms to develop intelligent decision trees

Decision tree algorithms have been widely used in dealing with data mining problems. However, scalability and efficiency are significant concerns in the implementation. We propose an innovative evolutionary computation approach combining statistical sampling, a genetic algorithm and a decision tree, to develop intelligent decision trees that alleviates some of these problems. Computational results show that our approach can obtain significantly better decision trees at lower sampling levels than the standard decision tree algorithm