New uncertainty measurement for hybrid data and its application in attribute reduction
H Huang, Z Li, F Liu, CF Wen - Information Sciences, 2024 - Elsevier
Due to limitations in data acquisition, data in real life often contains a wealth of uncertain
information. Uncertainty measurement (UM) constructed within the framework of rough set …
information. Uncertainty measurement (UM) constructed within the framework of rough set …
Neighborhood multigranulation rough sets for cost-sensitive feature selection on hybrid data
W Shu, Q Xia, W Qian - Neurocomputing, 2024 - Elsevier
Feature selection is a vital preprocessing step in real applications of data mining and
machine learning. With the prevalence of high-dimensional hybrid data sets in real-world …
machine learning. With the prevalence of high-dimensional hybrid data sets in real-world …
Distance metric learning-based multi-granularity neighborhood rough sets for attribute reduction
S Cui, G Li, B Sang, W Xu, H Chen - Applied Soft Computing, 2024 - Elsevier
Attribute reduction is a hot research topic in data mining, in which rough set theory-based
attribute reduction methods have been widely focused. The neighborhood rough set (NRS) …
attribute reduction methods have been widely focused. The neighborhood rough set (NRS) …
Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measure
S Solorio-Fernández, JA Carrasco-Ochoa… - Neurocomputing, 2024 - Elsevier
Abstract In recent years, Unsupervised Feature Selection (UFS) methods have attracted
considerable interest in different research areas due to their wide application in problems …
considerable interest in different research areas due to their wide application in problems …
Neighborhood relation-based incremental label propagation algorithm for partially labeled hybrid data
W Shu, D Cao, W Qian, S Li - Machine Learning, 2024 - Springer
Label propagation can rapidly predict the labels of unlabeled objects as the correct answers
from a small amount of given label information, which can enhance the performance of …
from a small amount of given label information, which can enhance the performance of …
Fast fixed granular-ball for attribute reduction in label noise environments and its application in medical diagnosis
X Peng, P Wang, Y Shao, Y Gong, J Qian - International Journal of …, 2024 - Springer
Although neighborhood rough set (NRS) based attribute reduction methods have achieved
excellent performance in many scenarios, the efficiency and robustness of these methods …
excellent performance in many scenarios, the efficiency and robustness of these methods …
Optimizing Attribute Reduction in Multi-Granularity Data through a Hybrid Supervised–Unsupervised Model
Z Fan, J Chen, H Cui, J Song, T Xu - Mathematics, 2024 - mdpi.com
Attribute reduction is a core technique in the rough set domain and an important step in data
preprocessing. Researchers have proposed numerous innovative methods to enhance the …
preprocessing. Researchers have proposed numerous innovative methods to enhance the …