Results 251 to 260 of about 173,381 (281)

NEIGHBORHOOD SYSTEM BASED ROUGH SET: MODELS AND ATTRIBUTE REDUCTIONS

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2012
The neighborhood system based rough set is a generalization of Pawlak's rough set model since the former uses the neighborhood system instead of the partition for constructing target approximation. In this paper, the neighborhood system based rough set approach is employed to deal with the incomplete information system. By the coverings induced by the
Yang, Xibei, Chen, Zehua, Dou, Huili, Zhang, Ming, Yang, Jingyu   +4 more
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Axiomatization on generalized neighborhood system-based rough sets

Soft Computing, 2017
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Zhao, Fangfang, Li, Lingqiang
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Uncertainty measures of Neighborhood System-based rough sets

Knowledge-Based Systems, 2015
We first study the uncertainty of rough sets in binary GrC Model (Third GrC Model).We first propose the rough memberships to depict rough sets.Construct fuzzy entropy based on rough memberships and prove its rationality.We first propose the rough intuitionistic memberships to depict rough sets.Construct intuitionistic fuzzy entropy to measure ...
Tingting Zheng, Linyun Zhu
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Rough sets, neighborhood systems and granular computing

Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411), 2003
Granulation of a universe involves grouping of similar elements into granules. With granulated views, we deal with approximations of concepts, represented by subsets of the universe, in terms of granules. This paper examines the problem of approximations with respect to various granulations of the universe. The granulation structures used by both rough
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Palmprint Recognition Based on Neighborhood Rough Set

2010
Feature extraction is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Neighborhood rough set (NRS) based feature extracting algorithm is able to delete most of the redundant and irrelevant features, which avoid the step of data discretization and hence decreased the information lost in preprocess. In
Shanwen Zhang, Jiandu Liu
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Neighborhood rough set reduction with fish swarm algorithm

Soft Computing, 2016
Feature reduction refers to the problem of deleting those input features that are less predictive of a given outcome; a problem encountered in many areas such as pattern recognition, machine learning and data mining. In particular, it has been successfully applied in tasks that involve datasets containing huge numbers of features.
Yumin Chen, Zhiqiang Zeng, Junwen Lu
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Label distribution feature selection based on neighborhood rough set

Concurrency and Computation: Practice and Experience
SummaryIn label distribution learning (LDL), an instance is involved with many labels in different importance degrees, and the feature space of instances is accompanied with thousands of redundant and/or irrelevant features. Therefore, the main characteristic of feature selection in LDL is to evaluate the ability of each feature.
Yilin Wu, Wenzhong Guo, Yaojin Lin
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Granulation and Nearest Neighborhoods: Rough Set Approach

2001
"Nearest" neighborhoods are informally used in many areas of AI and database. Mathematically, a "nearest" neighborhood system that maps each object p a unique crisp/fuzzy subset of data, representing the "nearest" neighborhood, is a binary relation between the object and data spaces. "Nearest" neighborhood consists of data that are semantically related
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Neighborhood rough set with neighborhood equivalence relation for feature selection

Knowledge and Information Systems, 2023
Shangzhi Wu, Litai Wang, Shuyue Ge, Zhengwei Hao, Yulin Liu   +4 more
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Local Weighted Generalized Multigranulation Neighborhood Rough Set

2021 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), 2021
Yanting Guo, Eric C.C. Tsang, Meng Hu
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