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

Mohammad Azad and Mikhail Moshkov

European Journal of Operational Research, 2017, vol. 263, issue 3, 910-921

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.

Keywords: Multiple criteria analysis; Dynamic programming; Decision trees; Inhibitory trees; Totally optimal trees (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:263:y:2017:i:3:p:910-921

DOI: 10.1016/j.ejor.2017.06.026

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