Finding the right decision tree's induction strategy for a hard real worldproblem

Decision trees have been already successfully used in medicine, but as in t raditional statistics, some hard real world problems can not be solved succ essfully using the traditional way of induction. In our experiments we test ed various methods for building univariate decision trees in order to find the best induction strategy. On a hard real world problem of the Orthopaedi c fracture data with 2637 cases, described by 23 attributes and a decision with three possible values, we built decision trees with four classical app roaches, one hybrid approach where we combined neural networks and decision trees, and with an evolutionary approach. The results show that ail approa ches had problems with either accuracy, sensitivity, or decision tree size. The comparison shows that the best compromise in hard real world problem d ecision trees building is the evolutionary approach. (C) 2001 Elsevier Scie nce Ireland Ltd. All rights reserved.