Learning decision trees for the partial label ranking problem
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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.
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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
Generalized Large Margin $k$NN for Partial Label Learning
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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