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
Permanent link to this recordhttp://hdl.handle.net/10754/564642
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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.