Results 241 to 250 of about 7,027,366 (305)

Local neighborhood rough set

Knowledge-Based Systems, 2018
With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data.
Qi Wang, Y. Qian, Xinyan Liang, Qian Guo, Jiye Liang   +4 more
semanticscholar   +1 more source

Attribute reduction for multi-label learning with fuzzy rough set

Knowledge-Based Systems, 2018
In multi-label learning, each sample is related to multiple labels simultaneously, and attribute space of samples is with high-dimensionality. Therefore, the key issue for attribute reduction in multi-label data is to measure the quality of each ...
Yaojin Lin, Yuwen Li, Chenxi Wang, Jinkun Chen   +3 more
semanticscholar   +1 more source

Online multi-label streaming feature selection based on neighborhood rough set

Pattern Recognition, 2018
Multi-label feature selection has grabbed intensive attention in many big data applications. However, traditional multi-label feature selection methods generally ignore a real-world scenario, i.e., the features constantly flow into the model one by one ...
Jinghua Liu, Yaojin Lin, Yuwen Li, Wei Weng, Shunxiang Wu   +4 more
semanticscholar   +1 more source

Rough Sets

2005
Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge, tackled for a long time by philosophers, logicians, and mathematicians, has become also a crucial issue for computer scientists, particularly in the area of artificial intelligence.
Zdzislaw Pawlak, Lech Polkowski, Andrzej Skowron   +2 more
openaire   +1 more source

An $N$-Soft Set Approach to Rough Sets

IEEE transactions on fuzzy systems, 2020
The philosophy of soft sets is founded on the fundamental idea of parameterization, while Pawlak's rough sets put more emphasis on the importance of granulation. As a multivalued extension of soft sets, the newly emerging concept called $N$-soft sets can
J. Alcantud, F. Feng, R. Yager
semanticscholar   +1 more source

Dynamic updating approximations of local generalized multigranulation neighborhood rough set

Applied intelligence (Boston), 2022
Weihua Xu, Kehua Yuan, Wentao Li
semanticscholar   +1 more source

Generalized textural rough sets: Rough set models over two universes

Information Sciences, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Altay Uğur, Ayşegül, Diker, Murat
openaire   +1 more source

Attribute reduction based on max-decision neighborhood rough set model

Knowledge-Based Systems, 2018
The neighborhood rough set model only focuses on the consistent samples whose neighborhoods are completely contained in some decision classes, and ignores the divisibility of the boundary samples whose neighborhoods can not be contained in any decision ...
Xiaodong Fan, Wei Zhao, Changzhong Wang, Yang Huang   +3 more
semanticscholar   +1 more source

Rough Sets

2009
This chapter discusses ways in which rough-set theory can enhance databases by allowing for the management of uncertainty. Rough sets can be integrated into an underlying database model, relational or object oriented, and also used in the design and uerying of databases, because roughsets are a versatile theory, theories.
Theresa Beaubouef, Frederick E Petry
openaire   +2 more sources

Attribute Selection for Partially Labeled Categorical Data By Rough Set Approach

IEEE Transactions on Cybernetics, 2017
Attribute selection is considered as the most characteristic result in rough set theory to distinguish itself to other theories. However, existing attribute selection approaches can not handle partially labeled data.
Jianhua Dai, Q. Hu, Jinghong Zhang, Hu Hu, Nenggan Zheng   +4 more
semanticscholar   +1 more source