Feature selection for online streaming high-dimensional data: A state-of-the-art review
Abstract Knowledge discovery for data streaming requires online feature selection to reduce
the complexity of real-world datasets and significantly improve the learning process. This is …
the complexity of real-world datasets and significantly improve the learning process. This is …
Group-preserving label-specific feature selection for multi-label learning
In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …
objects naturally belong to more than one class label, giving rise to the multi-label learning …
Feature-specific mutual information variation for multi-label feature selection
Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …
regarding multi-label data, which attracts wide attention for feature selection. Feature …
A survey on multi-label feature selection from perspectives of label fusion
W Qian, J Huang, F Xu, W Shu, W Ding - Information Fusion, 2023 - Elsevier
With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …
multi-label data have become prevalent in various fields. However, these datasets often …
MFSJMI: Multi-label feature selection considering join mutual information and interaction weight
P Zhang, G Liu, J Song - Pattern Recognition, 2023 - Elsevier
Multi-label feature selection captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …
dimensional multi-label data, which plays an important role in pattern recognition. In …
Multi-objective PSO based online feature selection for multi-label classification
Feature selection approaches aim to select a set of prominent features that best describe the
data to improve the efficiency without degrading the performance of the model. In many real …
data to improve the efficiency without degrading the performance of the model. In many real …
A unified low-order information-theoretic feature selection framework for multi-label learning
The approximation of low-order information-theoretic terms for feature selection approaches
has achieved success in addressing high-dimensional multi-label data. However, three …
has achieved success in addressing high-dimensional multi-label data. However, three …
Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation
Recent years, joint feature selection and multi-label learning have received extensive
attention as an open problem. However, there exist three general issues in previous multi …
attention as an open problem. However, there exist three general issues in previous multi …
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 …
Fuzzy mutual information-based multilabel feature selection with label dependency and streaming labels
Multilabel feature selection (MFS) has received widespread attention in various big data
applications. However, most of the existing methods either explicitly or implicitly assume that …
applications. However, most of the existing methods either explicitly or implicitly assume that …