Optimization of decision rule complexity for decision tables with many-valued decisions
Type
Conference Paper
KAUST Department
Applied Mathematics and Computational Science ProgramComputational 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
Date
2013-10Permanent link to this record
http://hdl.handle.net/10754/564808Metadata
Show full item recordAbstract
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.Citation
Azad, M., Chikalov, I., & Moshkov, M. (2013). Optimization of Decision Rule Complexity for Decision Tables with Many-Valued Decisions. 2013 IEEE International Conference on Systems, Man, and Cybernetics. doi:10.1109/smc.2013.81Conference/Event name
2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013ISBN
9780769551548Scopus Count
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