Abstract
In rough set–based feature selection, the discernibility matrix provides a mathematical framework for computing all or multiple reducts. However, for applications such as ensemble model induction, there is no need to generate an exhaustive set of reducts; instead, selecting a smaller subset with better individual performance and sufficient diversity tends to more effective. Diversity among base classifiers is critical for improving the predictive performance of ensemble models, yet most existing rough set–based methods do not explicitly address this aspect when generating multiple reducts. To overcome this limitation, this paper proposes two strategies that embed diversity directly into the reduct generation process. The first introduces a novel partition refinement cardinality heuristic that selects mutually exclusive reducts with maximum partition cardinality differences to promote classifier diversity. The second presents an efficient adaptation of an existing least overlap heuristic, combined with an incremental construction of the absorbed discernibility matrix to ensure scalability for large datasets where conventional discernibility matrix construction is infeasible. Finally, empirical analysis with state-of-the-art algorithms demonstrates that the diverse reducts generated by the proposed methods successfully achieve their goal of enhancing ensemble model performance through improved diversity and predictive accuracy.
