Decision Rules, Trees and Tests for Tables with Many-valued Decisions–comparative Study
KAUST DepartmentApplied Mathematics and Computational Science Program
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Office of the VP
Online Publication Date2013-10-04
Print Publication Date2013
Permanent link to this recordhttp://hdl.handle.net/10754/552480
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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.
Decision and Inhibitory Rule Optimization for Decision Tables with Many-valued DecisionsAlsolami, Fawaz (2016-04-25) [Dissertation]
Advisor: Moshkov, Mikhail
Committee members: Bajic, Vladimir B.; Suraj, Zbigniew; Zhang, Xiangliang‘If-then’ rule sets are one of the most expressive and human-readable knowledge representations. This thesis deals with optimization and analysis of decision and inhibitory rules for decision tables with many-valued decisions. The most important areas of applications are knowledge extraction and representation. The benefit of considering inhibitory rules is connected with the fact that in some situations they can describe more knowledge than the decision ones. Decision tables with many-valued decisions arise in combinatorial optimization, computational geometry, fault diagnosis, and especially under the processing of data sets. In this thesis, various examples of real-life problems are considered which help to understand the motivation of the investigation. We extend relatively simple results obtained earlier for decision rules over decision tables with many-valued decisions to the case of inhibitory rules. The behavior of Shannon functions (which characterize complexity of rule systems) is studied for finite and infinite information systems, for global and local approaches, and for decision and inhibitory rules. The extensions of dynamic programming for the study of decision rules over decision tables with single-valued decisions are generalized to the case of decision tables with many-valued decisions. These results are also extended to the case of inhibitory rules. As a result, we have algorithms (i) for multi-stage optimization of rules relative to such criteria as length or coverage, (ii) for counting the number of optimal rules, (iii) for construction of Pareto optimal points for bi-criteria optimization problems, (iv) for construction of graphs describing relationships between two cost functions, and (v) for construction of graphs describing relationships between cost and accuracy of rules. The applications of created tools include comparison (based on information about Pareto optimal points) of greedy heuristics for bi-criteria optimization of rules, and construction (based on multi-stage optimization of rules) of relatively short systems of rules that can be used for knowledge representation.
Optimization of decision rule complexity for decision tables with many-valued decisionsAzad, Mohammad; Chikalov, Igor; Moshkov, Mikhail (2013 IEEE International Conference on Systems, Man, and Cybernetics, Institute of Electrical and Electronics Engineers (IEEE), 2013-10) [Conference Paper]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 (Rough Sets and Knowledge Technology, Springer Nature, 2011) [Conference Paper]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.