Abstract
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∗RSOR 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 MA∗_RSOR through analyzing the search process’s behaviour in A∗RSOR 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 MA∗_RSOR is demonstrated through theoretical analysis and comparative experimental validation with state-of-the-art algorithms. The experimental results with benchmark data sets established that MA∗_RSOR achieves significant computational time gain (49 – 99 %) and space reduction (37 – 96 %) over A∗RSOR. The MA∗_RSOR 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 CVNonCore heuristic is proposed for predicting when the MA∗_RSOR 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.
