Restricted multi-pruning of decision trees

The trade-off between the decision tree size and good classification accuracy is a research challenge. It can be achieved if we create multiple pruned trees from the set of Pareto optimal points using dynamic programming approach (multi-pruning process). However, this process can be extensively slow. We consider a modification of the multi-pruning process (restricted multi-pruning) that requires less memory and time but usually keeps the accuracy of the constructed classifiers.