Decision tree´s induction strategies evaluated on a hard real world problem

Decision trees have been already been successfully used in medicine, but as in traditional statistics, some hard real-world problems cannot be solved successfully using the traditional method of induction. In our experiments, we tested various methods for building univariate decision trees in order to find the best induction strategy. On a hard real-world problem concerning orthopaedic fracture data, with 2637 cases described by 23 attributes and a decision with three possible values, we built decision trees with four classical approaches, with a hybrid approach (where we combined neural networks and decision trees) and with an evolutionary approach. The results show that all the approaches had problems with either accuracy or decision tree size. The comparison shows that the best compromise in hard real-world decision-tree building is the evolutionary approach