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

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.

Citation
Azad 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.

Acknowledgements
Research 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.

Publisher
Elsevier BV

Journal
European Journal of Operational Research

DOI
10.1016/j.ejor.2017.06.026

Additional Links
http://www.sciencedirect.com/science/article/pii/S0377221717305659

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