Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study
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AbstractIn this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.
CitationDecision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study 2013, 22:87 Procedia Computer Science
JournalProcedia Computer Science
Conference/Event name17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013
Showing items related by title, author, creator and subject.
Optimization of decision rule complexity for decision tables with many-valued decisionsAzad, Mohammad; Chikalov, Igor; Moshkov, Mikhail (Institute of Electrical and Electronics Engineers (IEEE), 2013-10)We describe new heuristics to construct decision rules for decision tables with many-valued decisions from the point of view of length and coverage which are enough good. We use statistical test to find leaders among the heuristics. After that, we compare our results with optimal result obtained by dynamic programming algorithms. The average percentage of relative difference between length (coverage) of constructed and optimal rules is at most 6.89% (15.89%, respectively) for leaders which seems to be a promising result. © 2013 IEEE.
Decision rules for decision tables with many-valued decisionsChikalov, Igor; Zielosko, Beata (Springer Science + Business Media, 2011)In the paper, authors presents a greedy algorithm for construction of exact and partial decision rules for decision tables with many-valued decisions. Exact decision rules can be 'over-fitted', so instead of exact decision rules with many attributes, it is more appropriate to work with partial decision rules with smaller number of attributes. Based on results for set cover problem authors study bounds on accuracy of greedy algorithm for exact and partial decision rule construction, and complexity of the problem of minimization of decision rule length. © 2011 Springer-Verlag.
Minimization of Decision Tree Average Depth for Decision Tables with Many-valued DecisionsAzad, Mohammad; Moshkov, Mikhail (Elsevier BV, 2014-09-13)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.