Optimization of inhibitory decision rules relative to length and coverage
KAUST DepartmentComputer, 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
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AbstractThe paper is devoted to the study of algorithms for optimization of inhibitory rules relative to the length and coverage. In contrast with usual rules that have on the right-hand side a relation "attribute ≠ value", inhibitory rules have a relation "attribute = value" on the right-hand side. The considered algorithms are based on extensions of dynamic programming. © 2012 Springer-Verlag.
PublisherSpringer Science + Business Media
Conference/Event name7th International Conference on Rough Sets and Knowledge Technology, RSKT 2012