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A review on multi-label learning algorithms
Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …
instance while associated with a set of labels simultaneously. During the past decade …
Multi‐label learning: a review of the state of the art and ongoing research
E Gibaja, S Ventura - Wiley Interdisciplinary Reviews: Data …, 2014 - Wiley Online Library
Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …
to improve performance in problems where a pattern may have more than one associated …
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 …
Manifold regularized discriminative feature selection for multi-label learning
In multi-label learning, objects are essentially related to multiple semantic meanings, and
the type of data is confronted with the impact of high feature dimensionality simultaneously …
the type of data is confronted with the impact of high feature dimensionality simultaneously …
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 …
Multi-label learning with global and local label correlation
It is well-known that exploiting label correlations is important to multi-label learning. Existing
approaches either assume that the label correlations are global and shared by all instances; …
approaches either assume that the label correlations are global and shared by all instances; …
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 …
Partial multi-label learning with noisy label identification
Partial multi-label learning (PML) deals with problems where each instance is assigned with
a candidate label set, which contains multiple relevant labels and some noisy labels. Recent …
a candidate label set, which contains multiple relevant labels and some noisy labels. Recent …
Learning common and label-specific features for multi-label classification with correlation information
J Li, P Li, X Hu, K Yu - Pattern recognition, 2022 - Elsevier
In multi-label classification, many existing works only pay attention to the label-specific
features and label correlation while they ignore the common features and instance …
features and label correlation while they ignore the common features and instance …
Label enhancement for label distribution learning
Label distribution is more general than both single-label annotation and multi-label
annotation. It covers a certain number of labels, representing the degree to which each label …
annotation. It covers a certain number of labels, representing the degree to which each label …