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 …
Multi-label feature selection based on label distribution and neighborhood rough set
Multi-label feature selection is an indispensable technology in multi-semantic high-
dimensional data preprocessing, which has been brought into focus in recent years …
dimensional data preprocessing, which has been brought into focus in recent years …
A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets
Attribute reduction is an important application of rough set theory. Most existing rough set
models do not consider the weight information of attributes in information systems. In this …
models do not consider the weight information of attributes in information systems. In this …
Feature selection in threes: neighborhood relevancy, redundancy, and granularity interactivity
As a fundamental granular computing strategy, neighborhood granulation has been
acknowledged as an intuitive and effective approach to feature evaluation and selection …
acknowledged as an intuitive and effective approach to feature evaluation and selection …
Variable radius neighborhood rough sets and attribute reduction
D Zhang, P Zhu - International Journal of Approximate Reasoning, 2022 - Elsevier
Neighborhood rough sets provide important insights into dealing with numerical data.
Neighborhood radius, a key factor that affects data uncertainty, is uniformly given in most of …
Neighborhood radius, a key factor that affects data uncertainty, is uniformly given in most of …
Mapreduce accelerated attribute reduction based on neighborhood entropy with apache spark
C Luo, Q Cao, T Li, H Chen, S Wang - Expert Systems with Applications, 2023 - Elsevier
Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …
field of data mining, which has gained much attention due to its ability to provide better …
A soft neighborhood rough set model and its applications
S An, X Guo, C Wang, G Guo, J Dai - Information Sciences, 2023 - Elsevier
Neighborhood rough set theory is widely used to measure the uncertainty of data in machine
learning and data mining. However, the neighborhood radius has a significant influence on …
learning and data mining. However, the neighborhood radius has a significant influence on …
Rapid detection of mussels contaminated by heavy metals using near-infrared reflectance spectroscopy and a constrained difference extreme learning machine
Y Liu, L Xu, S Zeng, F Qiao, W Jiang, Z Xu - Spectrochimica Acta Part A …, 2022 - Elsevier
The consumption of mussels contaminated with heavy metals can cause toxicity in humans.
To realize quick, accurate, and non-destructive detection of heavy metals in mussels, a new …
To realize quick, accurate, and non-destructive detection of heavy metals in mussels, a new …
Feature selection with local density-based fuzzy rough set model for noisy data
Fuzzy rough set theory can model uncertainty in data and has been applied to feature
selection for machine learning tasks. The existence of noise in data is one of the reasons for …
selection for machine learning tasks. The existence of noise in data is one of the reasons for …
A new method for feature selection based on weighted k-nearest neighborhood rough set
N Wang, E Zhao - Expert Systems with Applications, 2024 - Elsevier
The neighborhood rough set theory is a helpful instrument for working with data that is
numerical, and the performance of its uncertainty measures is generally stable. Even one …
numerical, and the performance of its uncertainty measures is generally stable. Even one …