Local attribute reductions for decision tables.

Attribute reduction is among the most important areas of research in rough sets. This paper investigates the types of local attribute reduction for decision tables. We propose the concepts of l th decision class lower approximation reduction, l th decision class reduction, and l th decision class β -reduction for decision tables, and provide their corresponding reduction algorithms via discernibility matrices. We also establish the relationship between positive-region reduction and the l th decision class β -reduction, and report a case study using the University of California–Irvine dataset to verify the theoretical results. [ABSTRACT FROM AUTHOR]