Learning decision trees for the partial label ranking problem
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22325
Abstract: The Label Ranking (LR) problem is a well‐known nonstandard supervised classification problem, the goal of which is to learn preference classifiers from data, mapping instances to rankings of the labels of the class variable. In… read more here.
Keywords: decision; partial label; label ranking; ranking problem ... See more keywords
Partial label learning via low-rank representation and label propagation
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DOI: 10.1007/s00500-019-04269-9
Abstract: In partial label learning, each training instance is assigned with a set of candidate labels, among which only one is correct. An intuitive strategy to learn from such ambiguous data is disambiguation. Existing methods following… read more here.
Keywords: label; rank representation; low rank; via low ... See more keywords
Partial label metric learning by collapsing classes
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DOI: 10.1007/s13042-020-01129-z
Abstract: Partial label learning (PLL) is a weakly supervised learning framework proposed recently, in which the ground-truth label of training sample is not precisely annotated but concealed in a set of candidate labels, which makes the… read more here.
Keywords: label; metric learning; pll; accuracy ... See more keywords
Imprecise Deep Forest for Partial Label Learning
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DOI: 10.1109/access.2020.3042838
Abstract: In partial label (PL) learning, each instance corresponds to a set of candidate labels, among which only one is valid. The objective of PL learning is to obtain a multi-class classifier from the training instances.… read more here.
Keywords: label; training; deep forest; ecoc algorithm ... See more keywords
A Self-Paced Regularization Framework for Partial-Label Learning.
Sign Up to like & getrecommendations! Published in 2020 at "IEEE transactions on cybernetics"
DOI: 10.1109/tcyb.2020.2990908
Abstract: Partial-label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate… read more here.
Keywords: label; paced regularization; framework; label learning ... See more keywords
MAGS: Max-Gap Loss-Guided Siamese-Reconstruction Network for Hyperspectral Image Partial Label Learning
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2025.3545949
Abstract: Due to the powerful feature extraction capabilities of deep learning, a series of deep learning-based methods for hyperspectral image (HSI) classification have been proposed and achieved satisfactory performance. However, most of these methods require a… read more here.
Keywords: label learning; hyperspectral image; partial label; gap loss ... See more keywords
Dimensionality Reduction for Partial Label Learning: A Unified and Adaptive Approach
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DOI: 10.1109/tkde.2024.3367721
Abstract: Partial label learning learns from instances with weak supervision, where each instance is associated with a set of candidate labels, among which only one is valid. Recently, dimensionality reduction has emerged as an effective preprocessing… read more here.
Keywords: partial label; label learning; label; dimensionality reduction ... See more keywords
Confidence-Induced Granular Partial Label Feature Selection via Dependency and Similarity
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DOI: 10.1109/tkde.2024.3405489
Abstract: Partial label learning (PLL) tackles scenarios where the unique ground-truth label of each sample is concealed within a candidate label set. Dimensionality reduction, considering labeling confidence estimation, has become a promising strategy to enhance the… read more here.
Keywords: dependency; feature; partial label; confidence ... See more keywords
Generalized Large Margin $k$NN for Partial Label Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2021.3109438
Abstract: To deal with noises in partial label learning (PLL), existing approaches try to perform disambiguation either by identifying the ground-truth label or by averaging the candidate labels. However, these methods can be easily misled by… read more here.
Keywords: partial label; large margin; pll; label ... See more keywords
DEER: Distribution Divergence-based Graph Contrast for Partial Label Learning on Graphs
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DOI: 10.1109/tmm.2024.3408038
Abstract: —Graph neural networks (GNNs) have emerged as powerful tools for graph classification tasks. However, contemporary graph classification methods are predominantly studied in fully supervised scenarios, while there could be label ambiguity and noise in real-world… read more here.
Keywords: divergence; deer; distribution divergence; partial label ... See more keywords