Abstract
Incomplete decision systems containing missing attribute values occur frequently in real world applications. This paper proposes IQRAIG-incomplete algorithm for reduct computation in Incomplete Decision Systems using a novel granular framework. The proposed granular framework enables computation of similarity classes for a set of objects simultaneously which helps in increased effienciency of computing positive region. The merits of the proposed algorithm over IFSPA-IPR algorithm has been demonstrated empirically. © 2012 Springer-Verlag.
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Sai Prasad, P. S. V. S., & Chillarige, R. R. (2012). Novel granular framework for attribute reduction in incomplete decision systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7694 LNAI, pp. 188–201). https://doi.org/10.1007/978-3-642-35455-7_18
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