Optimization of decision rule complexity for decision tables with many-valued decisions

Handle URI:
http://hdl.handle.net/10754/564808
Title:
Optimization of decision rule complexity for decision tables with many-valued decisions
Authors:
Azad, Mohammad ( 0000-0001-9851-1420 ) ; Chikalov, Igor; Moshkov, Mikhail ( 0000-0003-0085-9483 )
Abstract:
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2013 IEEE International Conference on Systems, Man, and Cybernetics
Conference/Event name:
2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Issue Date:
Oct-2013
DOI:
10.1109/SMC.2013.81
Type:
Conference Paper
ISBN:
9780769551548
Appears in Collections:
Conference Papers; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAzad, Mohammaden
dc.contributor.authorChikalov, Igoren
dc.contributor.authorMoshkov, Mikhailen
dc.date.accessioned2015-08-04T07:16:42Zen
dc.date.available2015-08-04T07:16:42Zen
dc.date.issued2013-10en
dc.identifier.isbn9780769551548en
dc.identifier.doi10.1109/SMC.2013.81en
dc.identifier.urihttp://hdl.handle.net/10754/564808en
dc.description.abstractWe 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectDecision rulesen
dc.subjectDynamic programmingen
dc.subjectGreedy heuristicsen
dc.subjectMany-valued decisionsen
dc.subjectOptimizationen
dc.titleOptimization of decision rule complexity for decision tables with many-valued decisionsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Groupen
dc.identifier.journal2013 IEEE International Conference on Systems, Man, and Cyberneticsen
dc.conference.date13 October 2013 through 16 October 2013en
dc.conference.name2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013en
dc.conference.locationManchesteren
kaust.authorAzad, Mohammaden
kaust.authorChikalov, Igoren
kaust.authorMoshkov, Mikhailen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.