Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions
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AbstractWe 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.
CitationAzad M, Moshkov M (2017) Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions. European Journal of Operational Research. Available: http://dx.doi.org/10.1016/j.ejor.2017.06.026.
SponsorsResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We are greatly indebted to the anonymous reviewers for useful comments and suggestions.