Towards instance-dependent label noise-tolerant classification: a probabilistic approach
Sign Up to like & getrecommendations! Published in 2018 at "Pattern Analysis and Applications"
DOI: 10.1007/s10044-018-0750-z
Abstract: Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. Existing label noise-tolerant learning machines were primarily designed to tackle class-conditional noise which occurs at random, independently from… read more here.
Keywords: label noise; dependent label; noise tolerant; instance dependent ... See more keywords
Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food.
Sign Up to like & getrecommendations! Published in 2021 at "Analytica chimica acta"
DOI: 10.1016/j.aca.2021.338245
Abstract: Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised… read more here.
Keywords: label noise; selection; spectroscopic data; variable selection ... See more keywords
Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment
Sign Up to like & getrecommendations! Published in 2021 at "Medical image analysis"
DOI: 10.1016/j.media.2021.102087
Abstract: Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than… read more here.
Keywords: label noise; knowledge; chest; abnormality ... See more keywords
BundleNet: Learning with Noisy Label via Sample Correlations
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2017.2782844
Abstract: Sequential patterns are important, because they can be exploited to improve the prediction accuracy of our classifiers. Sequential data, such as time series/video frames, and event data are becoming more and more ubiquitous in a… read more here.
Keywords: label noise; bundlenet learning; noise; bundle module ... See more keywords
A Recursive Ensemble Learning Approach With Noisy Labels or Unlabeled Data
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2904403
Abstract: For many tasks, the successful application of deep learning relies on having large amounts of training data, labeled to a high standard. But much of the data in real-world applications suffer from label noise. Data… read more here.
Keywords: approach noisy; ensemble learning; approach; recursive ensemble ... See more keywords
Making Deep Neural Networks Robust to Label Noise: Cross-Training With a Novel Loss Function
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2940653
Abstract: Deep neural networks (DNNs) have achieved astonishing results on a variety of supervised learning tasks owing to a large scale of well-labeled training data. However, as recent researches have pointed out, the generalization performance of… read more here.
Keywords: loss; training; loss function; cross training ... See more keywords
Meta-Probability Weighting for Improving Reliability of DNNs to Label Noise
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2023.3237033
Abstract: Training noise-robust deep neural networks (DNNs) in label noise scenario is a crucial task. In this paper, we first demonstrates that the DNNs learning with label noise exhibits over-fitting issue on noisy labels because of… read more here.
Keywords: noise; label noise; dnns label; meta probability ... See more keywords
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3226371
Abstract: The development of accurate methods for multi-label scene classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. To address MLC problems, the use of deep neural networks… read more here.
Keywords: remote sensing; label noise; multi label; label ... See more keywords
Probabilistic Information-Theoretic Discriminant Analysis for Industrial Label-Noise Fault Diagnosis
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2020.3001335
Abstract: Fault diagnosis, which aims to identify the root cause of the observed abnormality, is essential for the control and optimization of industrial processes. Many existing data-driven fault diagnosis methods require all the training samples to… read more here.
Keywords: label noise; fault diagnosis; discriminant analysis; information ... See more keywords
Manifold-Preserving Sparse Graph-Based Ensemble FDA for Industrial Label-Noise Fault Classification
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Instrumentation and Measurement"
DOI: 10.1109/tim.2019.2930157
Abstract: For the fault classification of chemical industries, the typical Fisher discriminant analysis (FDA) model requires that all the training samples should be correctly labeled. Actually, training samples tend to be polluted by mislabeled samples for… read more here.
Keywords: fault classification; manifold preserving; training samples; label noise ... See more keywords
Robust Distance Metric Learning via Bayesian Inference
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2017.2782366
Abstract: Distance metric learning (DML) has achieved great success in many computer vision tasks. However, most existing DML algorithms are based on point estimation, and thus are sensitive to the choice of training examples and tend… read more here.
Keywords: bayesian inference; label noise; metric learning; distance metric ... See more keywords