Titlebook: Rough Set and Knowledge Technology; 5th International Co Jian Yu,Salvatore Greco,Andrzej Skowron Conference proceedings 2010 Springer-Verla

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1-vs-Others Rough Decision Forestthe same way as that for inducing a decision tree on the whole data, in which all possible classes are dealt with together. In such induced trees, some minority classes may be covered up by others when some branches grow or are pruned. For a multi-class problem, this paper proposes to induce individ

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Knowledge Reduction in Random Incomplete Information Systems via Evidence Theorymation systems based on Dempster-Shafer theory of evidence. The concepts of random belief reducts and random plausibility reducts in random incomplete information systems are introduced. The relationships among the random belief reduct, the random plausibility reduct, and the classical reduct are ex

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Knowledge Reduction Based on Granular Computing from Decision Information Systemseir own limitations. To address these problems, in this article, by applying the technique of granular computing, provided some rigorous and detailed proofs, and discussed the relationship between granular reduct introduced and knowledge reduction based on positive region related to simplicity decis

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Pattern Classification Using Class-Dependent Rough-Fuzzy Granular Spaceerve better class discriminatory information. Neighborhood rough sets (NRS) are used in the selection of a subset of granulated features that explore the local/contextual information from neighbor granules. The model thus explores mutually the advantages of class-dependent fuzzy granulation and NRS

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Ordered Weighted Average Based Fuzzy Rough Sets. Consequently, when applied to data analysis problems, these approximations are sensitive to noisy and/or outlying samples. In this paper, we advocate a mitigated approach, in which membership to the lower and upper approximation is determined by means of an aggregation process using ordered weight

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On Attribute Reduction of Rough Set Based on Pruning Rulesstimate method and fitness function in the processing of reduction was designed, and a new reduction algorithm based on pruning rules was developed, the complexity was analyzed, furthermore, many examples was given. The experimental results demonstrate that the developed algorithm can got the simple

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