Abstract
This paper presents a new attribute reduction algorithm, ARIMC, for both consistent and inconsistent decision tables. ARIMC eliminates all redundant and inconsistent objects in a decision table, extracts the core attributes when they exist in the decision table in an efficient way, and utilizes the core attributes and their absorptivity as the optimization condition to construct items of the discernibility matrix. Compared with Skowron et al's reduction algorithm [2], ARIMC shows its advantages in simplicity, practicability and time efficiency.
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