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Published in 2022 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22861
Abstract: In recent years, it has been difficult for multilabel classification to obtain complete multilabel data in real‐world applications, and even a large number of labels for training samples are randomly missed. As a result, the…
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Keywords:
classification;
multilabel classification;
two stage;
missing labels ... See more keywords
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Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3014916
Abstract: Recently, multilabel classification algorithms play an increasingly significant role in data mining and machine learning. However, some existing mutual information-based algorithms ignore the influence of the proportions of labels on the correlation degree between features…
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Keywords:
relieff;
mutual information;
multilabel classification;
multilabel ... See more keywords
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Published in 2022 at "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"
DOI: 10.1109/jstars.2022.3145042
Abstract: It is hard to use a single label to describe an image for the complexity of remote sensing scenes. Thus, it is a more general and practical choice to use multilabel image classification for high-resolution…
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Keywords:
relation;
semantic relation;
multilabel;
remote sensing ... See more keywords
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Published in 2021 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2019.2932439
Abstract: Multilabel classification deals with instances assigned with multiple labels simultaneously. It focuses on learning a mapping from feature space to label a space for out-of-sample extrapolation. The mapping can be seen as a feature selection…
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Keywords:
fine tuned;
specific features;
label;
multilabel classification ... See more keywords
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Published in 2017 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2016.2637169
Abstract: In this paper, a data-driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multilabel classification problem, with each label corresponding to one specific…
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Keywords:
mixed eccentricity;
classification;
framework;
use multilabel ... See more keywords
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Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3164906
Abstract: Most existing multilabel classification methods are batch learning methods, which may suffer from expensive retraining costs when dealing with new incoming data. In order to overcome the drawbacks of batch learning, we develop a family…
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Keywords:
multilabel classification;
multilabel;
classification algorithms;
online passive ... See more keywords
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Published in 2022 at "Symmetry"
DOI: 10.3390/sym14020286
Abstract: In multilabel classification, each sample can be allocated to multiple class labels at the same time. However, one of the prominent problems of multilabel classification is missing labels (incomplete labels) in multilabel text. The multilabel…
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Keywords:
classification;
multilabel classification;
missing label;
graph based ... See more keywords