Novel multi-label feature selection via label symmetric uncertainty correlation learning and feature redundancy evaluation
J Dai, J Chen, Y Liu, H Hu - Knowledge-Based Systems, 2020 - Elsevier
Multi-label data with high dimensionality, widely existed in the real world, bring many
challenges to the applications of machine learning, pattern recognition and other fields …
challenges to the applications of machine learning, pattern recognition and other fields …
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 …
Distinguishing two types of labels for multi-label feature selection
Multi-label feature selection plays an important role in pattern recognition, which can
improve multi-label classification performance. In traditional multi-label feature selection …
improve multi-label classification performance. In traditional multi-label feature selection …
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-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 …
Multi-label feature selection with constraint regression and adaptive spectral graph
Like single-label learning, multi-label learning also suffers from the curse of dimensionality.
Due to the existence of high-dimensional data, feature selection as a preprocessing tool …
Due to the existence of high-dimensional data, feature selection as a preprocessing tool …
Multilabel feature selection based on relative discernibility pair matrix
E Yao, D Li, Y Zhai, C Zhang - IEEE Transactions on Fuzzy …, 2021 - ieeexplore.ieee.org
In multilabel learning, the curse of dimensionality is one of major challenges. Existing single-
label feature selection methods cannot be directly applied to multilabel data, and multilabel …
label feature selection methods cannot be directly applied to multilabel data, and multilabel …
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 …
Multi-label feature selection based on max-dependency and min-redundancy
Multi-label learning deals with data belonging to different labels simultaneously. Like
traditional supervised feature selection, multi-label feature selection also plays an important …
traditional supervised feature selection, multi-label feature selection also plays an important …
Multi-label feature selection considering label supplementation
Multi-label feature selection is an efficient technique to alleviate the high dimensionality for
multi-label learning. Existing multi-label feature selection methods based on information …
multi-label learning. Existing multi-label feature selection methods based on information …