TY - JOUR
T1 - Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions
AU - Azad, Mohammad
AU - Moshkov, Mikhail
N1 - KAUST Repository Item: Exported on 2020-10-01
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.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/625062
UR - http://www.sciencedirect.com/science/article/pii/S0377221717305659
UR - http://www.scopus.com/inward/record.url?scp=85021200212&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2017.06.026
DO - 10.1016/j.ejor.2017.06.026
M3 - Article
SN - 0377-2217
VL - 263
SP - 910
EP - 921
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 3
ER -