Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4959349 | European Journal of Operational Research | 2017 | 41 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Mohammad Azad, Mikhail Moshkov,