Restricted multi-pruning of decision trees
Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Shahid Hussain · Data Science and Knowledge Engineering for Sensing Decision Support · 2018
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.