Double-quantitative feature selection approach for multi-granularity ordered decision systems
Double-quantitative-based granular computing implies the systematic perspective,
completeness, and accuracy of rough approximation. However, most of the existing research …
completeness, and accuracy of rough approximation. However, most of the existing research …
A two-way accelerator for feature selection using a monotonic fuzzy conditional entropy
Y Yang, D Chen, Z Ji, X Zhang, L Dong - Fuzzy Sets and Systems, 2024 - Elsevier
Fuzzy rough set is a highly effective mathematical method for feature selection, which offers
clear interpretability without expert knowledge. However, most of fuzzy-rough feature …
clear interpretability without expert knowledge. However, most of fuzzy-rough feature …
GAEFS: Self-supervised Graph Auto-encoder enhanced Feature Selection
Feature selection is an essential process in machine learning in selecting the features that
contribute the most to the prediction target to build more interpretable and robust models …
contribute the most to the prediction target to build more interpretable and robust models …
Geodesic Fuzzy Rough Sets for Discriminant Feature Extraction
Feature extraction is a fundamental and challenging task in machine learning, which aims at
extracting a subset of significant and discriminant features from raw data for various …
extracting a subset of significant and discriminant features from raw data for various …
A method of data analysis based on division-mining-fusion strategy
Q Kong, W Wang, W Xu, C Yan - Information Sciences, 2024 - Elsevier
With the advancement of data technology and storage services, the scale and complexity of
data are rapidly growing. Consequently, promptly analyzing data and deriving precise …
data are rapidly growing. Consequently, promptly analyzing data and deriving precise …
(I, O)-Fuzzy Rough Sets Based on Overlap Functions with Their Applications to Feature Selection and Image Edge Extraction
X Zhang, M Li, S Shao, J Wang - IEEE Transactions on Fuzzy …, 2023 - ieeexplore.ieee.org
As an important kind of aggregation function, overlap functions are widely used in
information fusion, data intelligence, image processing, decision science, etc. It is also used …
information fusion, data intelligence, image processing, decision science, etc. It is also used …
Efficient and Fast Algorithm for Attribute Reduction of Large Dimensional Data Using Rough Set Theory on Graphics Processing Unit
VKH Turaga, S Chebrolu - Arabian Journal for Science and Engineering, 2024 - Springer
Attribute reduction or attribute subset selection is among the highly important, and essential
data pre-processing tasks in all the applications belonging to various domains of …
data pre-processing tasks in all the applications belonging to various domains of …
CoSaR: Combating Label Noise Using Collaborative Sample Selection and Adversarial Regularization
Learning with noisy labels is nontrivial for deep learning models. Sample selection is a
widely investigated research topic for handling noisy labels. However, most existing …
widely investigated research topic for handling noisy labels. However, most existing …
Feature selection based on neighborhood rough sets and Gini index
Y Zhang, B Nie, J Du, J Chen, Y Du, H Jin… - PeerJ Computer …, 2023 - peerj.com
Neighborhood rough set is considered an essential approach for dealing with incomplete
data and inexact knowledge representation, and it has been widely applied in feature …
data and inexact knowledge representation, and it has been widely applied in feature …
Feature selection based on neighborhood rough sets and Gini index
Y Zhang, B Nie, J Du, J Chen, Y Du, H Jin, X Zheng… - europepmc.org
Neighborhood rough set is considered an essential approach for dealing with incomplete
data and inexact knowledge representation, and it has been widely applied in feature …
data and inexact knowledge representation, and it has been widely applied in feature …