An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution
Feature selection based on neighborhood rough set is a noteworthy step in dealing with
numerical data. Information entropy, proven in many theoretical analysis and practical …
numerical data. Information entropy, proven in many theoretical analysis and practical …
GBNRS: A novel rough set algorithm for fast adaptive attribute reduction in classification
S Xia, H Zhang, W Li, G Wang, E Giem… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …
A novel approach to attribute reduction based on weighted neighborhood rough sets
M Hu, ECC Tsang, Y Guo, D Chen, W Xu - Knowledge-Based Systems, 2021 - Elsevier
Neighborhood rough sets based attribute reduction, as a common dimension reduction
method, has been widely used in machine learning and data mining. Each attribute has the …
method, has been widely used in machine learning and data mining. Each attribute has the …
Dynamic updating approximations of local generalized multigranulation neighborhood rough set
The approximation space in rough set theory is important for dealing with uncertainties. As
the information contained in various information systems is constantly updated and changed …
the information contained in various information systems is constantly updated and changed …
A novel hybrid feature selection method considering feature interaction in neighborhood rough set
The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …
performances of learning models. Nevertheless, most feature selection methods do not take …
A novel outcome evaluation model of three-way decision: a change viewpoint
Abstract Three-way decision (3WD) is a granulation-computing paradigm consisting of
trisecting, acting, and outcome (ie, TAO model). Movement-based three-way decision (M …
trisecting, acting, and outcome (ie, TAO model). Movement-based three-way decision (M …
Neighborhood rough sets with distance metric learning for feature selection
Neighborhood rough set is a useful mathematic tool to describe uncertainty in mixed data.
Feature selection based on neighborhood rough set has been studied widely. However …
Feature selection based on neighborhood rough set has been studied widely. However …
Incremental feature selection for dynamic hybrid data using neighborhood rough set
W Shu, W Qian, Y Xie - Knowledge-Based Systems, 2020 - Elsevier
Feature selection with rough sets aims to delete redundant conditional features from static
data by considering single type features. However, traditional feature selection methods …
data by considering single type features. However, traditional feature selection methods …
Probability granular distance-based fuzzy rough set model
S An, Q Hu, C Wang - Applied Soft Computing, 2021 - Elsevier
Fuzzy rough set theory is sensitive to noisy samples as the fuzzy approximations are
proposed based on sensitive statistics, ie minimum and maximum. Here, we develop a …
proposed based on sensitive statistics, ie minimum and maximum. Here, we develop a …
RCAR-UNet: Retinal vessel segmentation network algorithm via novel rough attention mechanism
The health status of the retinal blood vessels is a significant reference for rapid and non-
invasive diagnosis of various ophthalmological, diabetic, and cardio-cerebrovascular …
invasive diagnosis of various ophthalmological, diabetic, and cardio-cerebrovascular …