Finding Optimal Rough Set Reduct with A* Search Algorithm

Abstract

Feature subset selection or reduct computation is a prominent domain for the classical rough set theory, which can preserve the most predictive features of a decision system. A given decision system has several reducts. Computation of all possible reducts was achieved through the computing prime implicants of the discernibility function. Currently, an optimal reduct based on any optimality criteria can only be achieved post-generation of all possible reducts. Indeed, it is an NP-hard problem. Several researchers have extended the alternative aspects with search strategies such as Genetic Algorithm, Ant Colony Optimization, Simulated Annealing, etc., for obtaining near-optimal reducts. In this paper, we propose an admissible and consistent heuristic for computing the optimal reduct having least number of induced equivalence classes or granules. $$A^*RSOR$$ reduct computation algorithm is developed using the proposed consistent heuristic in $$A^*$$ search. The proposed approach is validated both theoretically and experimentally. The comparative results establish the relevance of the proposed optimality criterion as the achieved optimal reduct has obtained significantly better accuracies with different classifiers.