Minimization of decision tree depth for multi-label decision tables

Mohammad Azad, Mikhail Moshkov

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

4 Scopus citations

Abstract

In this paper, we consider multi-label decision tables that have a set of decisions attached to each row. Our goal is to find one decision from the set of decisions for each row by using decision tree as our tool. Considering our target to minimize the depth of the decision tree, we devised various kinds of greedy algorithms as well as dynamic programming algorithm. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of depth of decision trees.
Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Granular Computing (GrC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages7-12
Number of pages6
ISBN (Print)9781479954643
DOIs
StatePublished - Oct 2014

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