A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets....
There are limitations in recent research undertaken on attribute reduction in incomplete decision systems. In this paper, we propose a distance-based method for attribute reduction in an incomplete decision system. In addition, we prove theoretically that our method is more effective than some other methods.
In fuzzy granular computing, a fuzzy granular structure is the collection of
fuzzy information granules and fuzzy information granularity is used to
measure the granulation degree of a fuzzy granular structure.
In general, the fuzzy information granularity characterizes discernibility ability
among fuzzy information granules in a fuzzy granular structure. In recent years,
researchers have proposed some concepts of fuzzy information granularity based
on partial order relations. However, the existing...
In recent years, rough set approach computing issues concerning
reducts of decision tables have attracted the attention of many researchers.
In this paper, we present the time complexity of an algorithm
computing reducts of decision tables by relational database approach. Let
DS = (U, C ∪ {d}) be a consistent decision table, we say that A ⊆ C is a
relative reduct of DS if A contains a reduct of DS. Let s = <C ∪ {d} , F>
be a relation schema on the attribute set C ∪ {d}, we say that A ⊆ C...
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