Building a High-quality Decision Tree with a Genetic Algorithm

In dealing with a very large data set, it might be impractical to construct a decision tree using all of the points. To overcome this impracticality, subsets of the original data set can be extracted, a tree can be constructed on each subset, and then parts of individual trees can be combined in a smart way to produce a final set of feasible trees. In this paper, we take trees generated by a commercial decision tree package and allow them to crossover and mutate (using a genetic algorithm) in order to generate trees of better quality. We conduct a computational study of our approach using a real-life marketing data set and find that our approach produces uniformly high-quality decision trees.