A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data

Atkinson, Gentry; Metsis, Vangelis

doi:10.1007/978-3-030-79150-6_38

Cited by 6 publications

(5 citation statements)

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“…Noisy labels of time series exist in various domains, including healthcare, transportation, and power systems [33]. Data in these domains often possess real-time characteristics, complexity, and high dimensionality, making them susceptible to noisy labels due to environment, equipment, or human factors.…”

Section: Discussionmentioning

confidence: 99%

“…Taïk et al [32] applied federated learning to power load prediction, resulting in cloud-side-end models that can be efficiently deployed. In real power scenarios, labeling relevant electricity consumption time series samples can be affected by problems such as sensor failures, data transmission delays, or signal interruptions, resulting in the inevitable issue of noisy labels (incorrect labels) [33]. However, existing studies [34,35] focus on the efficient collaboration of cloud-side-end systems, paying less attention to the effective mining of low-quality time series data containing missing values, anomalies, and noisy labels in cloud-side-end interaction scenarios.…”

Section: Electrical Power Edge-end Interaction Modelingmentioning

confidence: 99%

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Electrical Power Edge-End Interaction Modeling with Time Series Label Noise Learning

Wang,

Zhou,

Zhao

et al. 2023

Electronics

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In the context of electrical power systems, modeling the edge-end interaction involves understanding the dynamic relationship between different components and endpoints of the system. However, the time series of electrical power obtained by user terminals often suffer from low-quality issues such as missing values, numerical anomalies, and noisy labels. These issues can easily reduce the robustness of data mining results for edge-end interaction models. Therefore, this paper proposes a time–frequency noisy label classification (TF-NLC) model, which improves the robustness of edge-end interaction models in dealing with low-quality issues. Specifically, we employ two deep neural networks that are trained concurrently, utilizing both the time and frequency domains. The two networks mutually guide each other’s classification training by selecting clean labels from batches within small loss data. To further improve the robustness of the classification of time and frequency domain feature representations, we introduce a time–frequency domain consistency contrastive learning module. By classifying the selection of clean labels based on time–frequency representations for mutually guided training, TF-NLC can effectively mitigate the negative impact of noisy labels on model training. Extensive experiments on eight electrical power and ten other different realistic scenario time series datasets show that our proposed TF-NLC achieves advanced classification performance under different noisy label scenarios. Also, the ablation and visualization experiments further demonstrate the robustness of our proposed method.

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Section: Discussionmentioning

confidence: 99%

Section: Electrical Power Edge-end Interaction Modelingmentioning

confidence: 99%

Electrical Power Edge-End Interaction Modeling with Time Series Label Noise Learning

Wang,

Zhou,

Zhao

et al. 2023

Electronics

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“…Real-world power scenarios introduce challenges like the high cost associated with data collection and annotation, giving rise to the obstacle of few-shot learning [32]. However, ongoing explorations [33,34] primarily focus on optimizing collaboration between cloud and end systems, with relatively less emphasis on effectively extracting meaningful patterns from sparsely labeled samples within the cloud-end interaction framework.…”

Section: Electrical Power Edge-end Interaction Modelingmentioning

confidence: 99%

Meta In-Context Learning: Harnessing Large Language Models for Electrical Data Classification

Zhou,

Li,

Zhang

et al. 2023

Energies

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The evolution of communication technology has driven the demand for intelligent power grids and data analysis in power systems. However, obtaining and annotating electrical data from intelligent terminals is time-consuming and challenging. We propose Meta In-Context Learning (M-ICL), a new approach that harnesses large language models to classify time series electrical data, which largely alleviates the need for annotated data when adapting to new tasks. The proposed M-ICL consists of two stages: meta-training and meta-testing. In meta-training, the model is trained on various tasks that have an adequate amount of training data. The meta-training stage aims to learn the mapping between electrical data and the embedding space of large language models. In the meta-testing stage, the trained model makes predictions on new tasks. By utilizing the in-context learning ability of large language models, M-ICL adapts models to new tasks effectively with only a few annotated instances (e.g., 1–5 training instances per class). Our contributions lie in the new application of large language models to electrical data classification and the introduction of M-ICL to improve the classification performance with the strong in-context learning ability of large language models. Furthermore, we conduct extensive experiments on 13 real-world datasets, and the experimental results show that the proposed M-ICL improves the average accuracy over all datasets by 19.06%, 12.06%, and 6.63% when only one, two, and five training instances for each class are available, respectively. In summary, M-ICL offers a promising solution to the challenges of electrical data classification.

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“…There has been a large body of work on identifying and correcting mislabeled instances in image classification (Karimi et al 2020). Time series data poses additional challenges, and nonrandom label noise present in real-world datasets has often been overlooked (Atkinson and Metsis 2021). A few approaches have been proposed to mitigate label noise in ECG signal classification.…”

Section: Noisy Labelsmentioning

confidence: 99%

“…Genetic optimization methods (Pasolli and Melgani 2015), cross-validation as an ensemble of machine learning classifiers to filter mislabeled instances (Li andCui 2019, Wu andTian 2020), and only keeping samples with high confidence of being correctly labeled (Stepien and Grzegorczyk 2017), are the approaches implemented for ECG data. Cleansing techniques have the drawback of increasing classifier bias and degrading accuracy, generalizing worse to label noise present in the test data (Atkinson and Metsis 2021). Wu and Tian (2020) have also implemented a semi-supervised clustering method to correct mislabeled training samples based on cross-validation and k-nearest neighbor (KNN) classification.…”

Section: Noisy Labelsmentioning

confidence: 99%

Label noise and self-learning label correction in cardiac abnormalities classification

et al. 2022

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Objective. Learning to classify cardiac abnormalities requires large and high-quality labeled datasets, which is a challenge in medical applications. Small datasets from various sources are often aggregated to meet this requirement, resulting in a final dataset prone to label noise owing to inter- and intra-observer variability, and different expertise. It is well known that label noise can affect the performance and generalizability of the trained models. In this work, we explore the impact of label noise and self-learning label correction on the classification of cardiac abnormalities on large heterogeneous datasets of electrocardiogram (ECG) signals. Approach. A state-of-the-art self-learning multi-class label correction method for image classification is adapted to learn a multi-label classifier for electrocardiogram signals. We evaluated our performance using 5-fold cross-validation on the publicly available PhysioNet/Computing in Cardiology (CinC) 2021 Challenge data, with full and reduced sets of leads. Due to the unknown label noise in the testing set, we tested our approach on the MNIST dataset. We investigated the performance under different levels of structured label noise for both datasets. Main results. Under high levels of noise, the cross-validation results of self-learning label correction showed an improvement of approximately 3% in the Challenge score for the PhysioNet/CinC 2021 Challenge dataset and, an improvement in accuracy of 5$\%$ and reduction of the expected calibration error of 0.03 for the MNIST dataset. We demonstrate that self-learning label correction can be used to effectively deal with the presence of unknown label noise, also when using a reduced number of ECG leads.

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A Survey of Methods for Detection and Correction of Noisy Labels in Time Series Data

Cited by 6 publications

References 50 publications

Electrical Power Edge-End Interaction Modeling with Time Series Label Noise Learning

Electrical Power Edge-End Interaction Modeling with Time Series Label Noise Learning

Meta In-Context Learning: Harnessing Large Language Models for Electrical Data Classification

Label noise and self-learning label correction in cardiac abnormalities classification

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