Titlebook: Rough Sets and Knowledge Technology; 9th International Co Duoqian Miao,Witold Pedrycz,Ruizhi Wang Conference proceedings 2014 Springer Inte

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An Explicit Sparse Mapping for Nonlinear Dimensionality Reduction-dimensional representation space. Previously, some methods have been proposed to provide explicit mappings for nonlinear dimensionality reduction methods. Nevertheless, a disadvantage of these methods is that the learned mapping functions are combinations of all the original features, thus it is of

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A Web-Based Learning Support System for Rough Sets environments. The learning subjects of Web-based learning systems are mostly for popular sciences. Little attention has been paid for learning cutting edge subjects and no such systems have been developed for rough sets. This paper presents the design principle, system architectures, and prototype

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0302-9743 in granular computing, big data to wise decisions, rough set theory, and three-way decisions, uncertainty, and granular computing.978-3-319-11739-3978-3-319-11740-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Approximate Reduction for the Interval-Valued Decision Table0MW unit in some power plant. Experimental results show that the algorithm proposed in this article can maintain a high classification accuracy with the proper parameters, and the numbers of objects and attributes can both be greatly reduced.

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An Explicit Sparse Mapping for Nonlinear Dimensionality Reductionreduction. By using this framework and the method of locally linear embedding, we derive an explicit sparse nonlinear dimensionality reduction algorithm, which is named sparse neighborhood preserving polynomial embedding. Experimental results on real world classification and clustering problems demonstrate the effectiveness of our approach.

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