Coarsest granularity-based optimal reduct using A search

The optimal reduct computation problem aims to obtain the best reduct out of all possible reducts of a given decision system. In the rough set literature, two optimality criteria exist for computing an optimal reduct: shortest length based and coarsest granular space based. The coarsest granular space-based optimal reduct has the ability to induce a better generalizable classification model. The A ∗ R S O R is an existing A ∗ search-based optimal reduct computation algorithm that uses the coarsest granular space as an optimality criterion. This article proposes an improved coarsest granularity-based optimal reduct approach M A ∗ _ R S O R through analyzing the search process’s behaviour in A ∗ R S O R algorithm. To minimize the search space utilization and arrive at an optimal reduct in less time, suitable modifications are incorporated using the domain knowledge of rough set theory. The relevance of M A ∗ _ R S O R is demonstrated through theoretical analysis and comparative experimental validation with state-of-the-art algorithms. The experimental results with benchmark data sets established that M A ∗ _ R S O R achieves significant computational time gain ( 49 - 99 % ) and space reduction ( 37 - 96 % ) over A ∗ R S O R . The M A ∗ _ R S O R could induce classification models with significantly better classification accuracies than state-of-the-art shortest length-based optimal/near-optimal reduct computation algorithms. In addition, a coefficient of variation based C V NonCore heuristic is proposed for predicting when the M A ∗ _ R S O R algorithm is appropriate to use. Experimental results validate the relevance of the heuristic as its prediction turned out correctly in 8 out of 10 data sets.