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For the computation of feature weights, this paper proposed a novel approach through introducing the concept of feature significance degree on the real rough set theory. The feature significance degree could characterize the contribution of features to the decision making more objectively. The proposed approach was applied to the benchmark test sets Reuters-21578 Top10 and 20 Newsgroups to examine...
Attribute discretization is one of the key issues for the Rough Set theory. First, a method is proposed to compute an initial cut points set. The indistinguishable relation of decision tables did not change, and the number of elements in the initial cut points set was reduced. Then, the cut point information entropy was defined to measure the importance of a cut point. Finally, an attribute discretization...
In the rough set model based on database systems, the algorithms of calculating cores and reducts are very efficient and scalable in data mining applications. However, one serious drawback of these algorithms is that they are only applicable for consistent decision tables. In this paper we propose an algorithm which is able to receive consistent decision tables by initial decision tables while at...
The performance of genetic algorithms (GAs) is dependent on many factors. In this paper, we have isolated one factor: the crossover operator. Commonly used crossover operators such as one-point, two-point and uniform crossover operator are likely to destroy the information obtained previously because of their random choices of crossover points. To overcome this defect, RSO, a new adaptive crossover...
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