Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions

Abstract
The paper is devoted to the analysis of greedy algorithms for the minimization of average depth of decision trees for decision tables such that each row is labeled with a set of decisions. The goal is to find one decision from the set of decisions. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of average depth of decision trees.

Citation
Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions 2014, 35:368 Procedia Computer Science

Publisher
Elsevier BV

Journal
Procedia Computer Science

Conference/Event Name
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014

DOI
10.1016/j.procs.2014.08.117

Additional Links
http://linkinghub.elsevier.com/retrieve/pii/S1877050914010825

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