Titlebook: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing; 15th International C Yiyu Yao,Qinghua Hu,Jerzy W. Grzymala-Busse Conference pr

Axon895

On Generalized Decision Functions: Reducts, Networks and Ensemblesell-known criteria for attribute selection and less-known results linking generalized decisions with the notions of multivalued dependency and conditional independence. We formulate the problem of finding the simplest ensembles of subsets of attributes which allow to retrieve original decision value

顾客

莎草

Multi-granularity Intelligent Information Processing learning, as a successful example model of multi-granularity computation, has made significant progress in the fields of face recognition, image automatic labeling, speech recognition, and so on. Its idea can be generalized as a model of solving problems by joint computing on multi-granular informa

鸵鸟

Granular Structures Induced by Interval Sets and Rough Setstriarchic theory of granular computing focuses on a multilevel and multi-view granular structure. This paper discusses granular structures of interval sets under inclusion relations between two interval sets from a measurement-theoretic perspective and set-theoretic perspective, respectively. From a

陶醉

reperfusion

alcoholism

A New Subsystem-Based Definition of Generalized Rough Set Modelew subsystem-based definition of generalized rough set model and disclose the corresponding properties. We also discuss the interrelationships between our definition and the existing ones, the outputs show that our definition is effective and reasonable.

爱好

A Comparison of Two Types of Covering-Based Rough Sets Through the Complement of Coveringsg-based rough sets defined by Zhu and ones defined by Xu and Zhang were compared with each other through binary relations. And the relationships between these two types were further explored by introducing a concept of complementary neighborhood. However, the essential connections between these two

单片眼镜

On the Nearness Measures of Near Setserties being discussed. The connections between nearness relations and rough approximations are surveyed. The notions of strong nearness relations with respect to indiscernibility relation and weak indiscernibility relation are introduced. Some new nearness measures with respect to nearness relation

elastic

Preclusivity and Simple Graphsling us to define the lower and upper preclusive approximations of graphs and two orthogonality graphs. Further, the possibility of defining the similarity lower and upper approximations and the sufficiency operator on graphs will be investigated, with particular attention to complete and bipartite