A Bi-criteria Optimization Model for Adjusting the Decision Tree Parameters

Decision trees play a very important role in knowledge representation because of its simplicity and self-explanatory nature. We study the optimization of the parameters of the decision trees to find a shorter as well as more accurate decision tree. Hence, we design two algorithms to build a decision tree with a given threshold of the number of vertices based on the bi-criteria optimization technique. Then, we calculate the local and global misclassification rates for these trees. Our goal is to study the effect of changing the threshold for the bi-criteria optimization of the decision trees. In the end, we recommend a range of thresholds that can give us more accurate decision trees with a reasonable number of vertices.