Sample-Label View Transfer Active Learning for Time Series Classification

Kinyua, P. I. D.; Jouandeau, Nicolas

doi:10.1007/978-3-030-86383-8_48

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Similarly, in 33, the loss of the representation learner is used to pick samples to label. Recently, Kinyua et al 11 presented a novel approach called transfer active learning, which together evaluates informativeness and representativeness of a time series sample‐label pair. They also demonstrate the efficacy of the proposed method in terms of exponentially reducing training costs.…”

Section: Related Workmentioning

confidence: 99%

Combining Representation Learning and Active Learning for Applications in Process Manufacturing

Dathari¹,

Stream

Klöpper³

2023

Chemie Ingenieur Technik

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Multivariate time series classification (TSC) is an important data‐driven modeling task in process industries. A common challenge of TSC in the process industry is the unavailability of labels. Active learning (AL) tackles this problem by incrementally obtaining as few labeled instances as possible by querying an oracle. From our initial experience of building an AL application we derive requirements on the machine learning approach in the context of such an interactive application. To meet the requirements, we combine AL with representation learning (RL): a model learns a latent space representation of unlabeled data, which is then used to train a classification model on the labeled data.

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Section: Related Workmentioning

confidence: 99%

Combining Representation Learning and Active Learning for Applications in Process Manufacturing

Dathari¹,

Stream

Klöpper³

2023

Chemie Ingenieur Technik

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“…They rely on self-training to select the most relevant samples for annotation and training the model. Also, the Transfer Active Learning (TAL) method selects the candidate samples by evaluating their informativeness and representativeness [206]. Continual learning, also known as lifelong learning, is a machine learning approach that enables models to learn continuously from new data without forgetting previous knowledge [207].…”

Section: Active Learningmentioning

confidence: 99%

Towards robust and label-efficient time series representation learning

Emadeldeen

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Using machine learning prediction models for quality control: a case study from the automotive industry

2023

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This paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. The results show that some holes can be predicted with good quality, and the predictions can be used to improve the quality control process. However, other holes show poor results and support the claim that real data problems are challenged by inappropriate information or a lack of relevant information.

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Sample-Label View Transfer Active Learning for Time Series Classification

Cited by 3 publications

References 17 publications

Combining Representation Learning and Active Learning for Applications in Process Manufacturing

Combining Representation Learning and Active Learning for Applications in Process Manufacturing

Towards robust and label-efficient time series representation learning

Using machine learning prediction models for quality control: a case study from the automotive industry

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