Restricted multi-pruning of decision trees

Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Shahid Hussain

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish (US)
Title of host publicationData Science and Knowledge Engineering for Sensing Decision Support
PublisherWorld Scientific
ISBN (Print)9789813273221
DOIs
StatePublished - Jul 30 2018

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