Optimization of decision rule complexity for decision tables with many-valued decisions
Type
Conference Paper
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionApplied Mathematics and Computational Science Program
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Date
2013-10Metadata
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.Conference/Event name
2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013ISBN
9780769551548Handle URI
http://hdl.handle.net/10754/564808Scopus Count
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