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ENHMM, Evaluation model of Web service health level, is constructed based on multidimensional network performance. The model supports to customize evaluation factors dynamically, and gives the methods of active measurement for the network performance based on the evaluation attributes. To evaluation algorithm, AiNet immune algorithm is used, in which a new mechanism of antibody promotion and suppression...
In order to develop reasonable modular product architecture with physically detachable modules, a new systematic method for product modularization is proposed based on axiomatic design (AD). In the proposed method, the product is decomposed hierarchically in its functional, physical and process domains according to AD. After the transformation from design matrices to design structure matrices, the...
The purpose of obtaining decision rules from decision information system is to gain the useful rules and inerratic knowledge through analyzing sample data set or data base. In this paper, an algorithm for obtaining decision rules based on greedy strategy is discussed under rough set view in consistent decision information system. The experimental results show that the algorithm that we proposed improves...
In order to construct a high-performance ensemble classifier, it needs that the basic classifiers, which contained by the ensemble one, have higher classification precision and their classification error is independent from each other. In fact, it is too difficult to choose these basic classifiers satisfying the two conditions above. Rough reduction is the core in the fields of Rough Set theory. Each...
Support vector machine (SVM) is a promising method of machine learning based on the structural risk minimization principle, which is characteristic of good generalization performance; Rough set (RS) is an effective tool to decrease data dimension in dealing with vagueness and uncertainty information. A SVM classifier based on RS reducts is researched in order to enhance the predicting performance...
Data mining and analysis algorithms are known to degrade in performance when facing with many redundant or irrelevant features. Attribute reduction is one of the primary problems of rough set theory, the goal of which is to delete irrelevant or unimportant information. Once all attribute reducts are got, the reasoning capability with multi attributes absent can behave well. Thus how to get all attribute...
Machine learning algorithms are known to degrade in performance when facing with many features that are not necessary in the field of artificial intelligence and pattern recognition. Rough set theory is a new effective tool in dealing with vagueness and uncertainty information. Attribute reduction is one of the most important concepts in rough set theory and application research. Once it gets the...
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