Greedy algorithms for construction of approximate tests for decision tables with many-valued decisions
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 a greedy algorithm for construction of approximate tests (super-reducts) This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions For a given row, we should find a decision from the set attached to this row We consider bounds on the precision of this algorithm relative to the cardinality of tests.