Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions
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ArticleAuthors
Azad, Mohammad
Moshkov, Mikhail

KAUST Department
Applied Mathematics and Computational Science ProgramComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2017-06-16Online Publication Date
2017-06-16Print Publication Date
2017-12Permanent link to this record
http://hdl.handle.net/10754/625062
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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.Sponsors
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 BVAdditional Links
http://www.sciencedirect.com/science/article/pii/S0377221717305659ae974a485f413a2113503eed53cd6c53
10.1016/j.ejor.2017.06.026