Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions
Fuzzy rough set theory is a powerful tool to deal with uncertainty information, which has
been successfully applied to the fields of attribute reduction, rule extraction, classification …
been successfully applied to the fields of attribute reduction, rule extraction, classification …
Learning correlation information for multi-label feature selection
In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …
characterized by high dimensional features, which contains complex correlation information …
MFGAD: Multi-fuzzy granules anomaly detection
Unsupervised anomaly detection is an important research direction in the process of
unsupervised knowledge acquisition. It has been successfully applied in many fields, such …
unsupervised knowledge acquisition. It has been successfully applied in many fields, such …
Multi-label feature selection based on label correlations and feature redundancy
The task of multi-label feature selection (MLFS) is to reduce redundant information and
generate the optimal feature subset from the original multi-label data. A variety of MLFS …
generate the optimal feature subset from the original multi-label data. A variety of MLFS …
MGFS: A multi-label graph-based feature selection algorithm via PageRank centrality
A Hashemi, MB Dowlatshahi… - Expert Systems with …, 2020 - Elsevier
In multi-label data, each instance corresponds to a set of labels instead of one label
whereby the instances belonging to a label in the corresponding column of that label are …
whereby the instances belonging to a label in the corresponding column of that label are …
Manifold learning with structured subspace for multi-label feature selection
Nowadays, multi-label learning is ubiquitous in practical applications, in which multi-label
data is always confronted with the curse of high-dimensional features. Feature selection has …
data is always confronted with the curse of high-dimensional features. Feature selection has …
Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems
L Sun, T Yin, W Ding, Y Qian, J Xu - Information Sciences, 2020 - Elsevier
Feature selection as an essential preprocessing step in multilabel classification has been
widely researched. Due to the diversity and complexity of multilabel datasets, some feature …
widely researched. Due to the diversity and complexity of multilabel datasets, some feature …
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 …
ASFS: A novel streaming feature selection for multi-label data based on neighborhood rough set
Neighborhood rough set based online streaming feature selection methods have aroused
wide concern in recent years and played a vital role in processing high-dimensional data …
wide concern in recent years and played a vital role in processing high-dimensional data …
Matrix factorization algorithm for multi-label learning with missing labels based on fuzzy rough set
J Deng, D Chen, H Wang, R Shi - Fuzzy Sets and Systems, 2024 - Elsevier
In multi-label learning, samples of practical classification task may associated with multiple
labels, it is challenging to acquire all labels of the training samples, the rapid expansion of …
labels, it is challenging to acquire all labels of the training samples, the rapid expansion of …