Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions

Mohammad Azad, Mikhail Moshkov

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

We study problems of optimization of decision and inhibitory trees for decision tables with many-valued decisions. As cost functions, we consider depth, average depth, number of nodes, and number of terminal/nonterminal nodes in trees. Decision tables with many-valued decisions (multi-label decision tables) are often more accurate models for real-life data sets than usual decision tables with single-valued decisions. Inhibitory trees can sometimes capture more information from decision tables than decision trees. In this paper, we create dynamic programming algorithms for multi-stage optimization of trees relative to a sequence of cost functions. We apply these algorithms to prove the existence of totally optimal (simultaneously optimal relative to a number of cost functions) decision and inhibitory trees for some modified decision tables from the UCI Machine Learning Repository.
Original languageEnglish (US)
Pages (from-to)910-921
Number of pages12
JournalEuropean Journal of Operational Research
Volume263
Issue number3
DOIs
StatePublished - Jun 16 2017

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