Abstract
Attribute reduction is one of the challenging problems in rough set theory. To accomplish an efficient reduction algorithm, this paper analyzes the shortcomings of the traditional methods based on attribute significance, and suggests a novel reduction way where the traditional attribute significance calculation is replaced by a special core attribute calculation. A decision table called the positive region sort ascending decision table (PR-SADT) is defined to optimize some key steps of the novel reduction method, including the special core attribute calculation, positive region calculation, etc. On this basis, a fast reduction algorithm is presented to obtain a complete positive region reduct. Experimental tests demonstrate that the novel reduction algorithm achieves obviously high computational efficiency.
Highlights
Due to the development of data collection technology, more objects and attributes are stored
Classical reduction methods are divided into three types, which are referred to as positive region reduction, boundary region reduction, and entropy based reduction, respectively [10]
We proposed a unique and innovative heuristic method, which applies a special core attribute calculation to replace the traditional attribute significance calculation
Summary
Due to the development of data collection technology, more objects and attributes are stored. Storing and processing all attributes could be very expensive and impractical computationally [1]. To address this issue, it is necessary to omit several attributes that will not seriously impact the resulting classification (recognition) error, cf [2]. The positive region reduction method ignores the discernibility relationship between rough granules [11,12,13,14]. The second type ignores the discernibility relationship between rough granules with the same decision value sets [15]. The third type ignores the discernibility relationship of rough granules with the same information entropy [16,17,18]. Positive region reduction is the most popular and widely used, especially for dynamic data sets and big data [19,20,21]
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