Active Learning with Clustering and Unsupervised Feature Learning

Berardo, Saul; Favero, Eloi Luiz; Neto, Nelson

doi:10.1007/978-3-319-18356-5_25

Cited by 4 publications

(6 citation statements)

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“…Berardo et al [2] utilize clustering in active learning for MNIST handwritten digits classification. They first employ unsupervised training for extracting features, then use these features for clustering samples.…”

Section: Related Workmentioning

confidence: 99%

Weakly Supervised Active Learning with Cluster Annotation

Perez¹,

Lebret²,

Aberer³

2018

Preprint

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In this work, we introduce a novel framework that employs cluster annotation to boost active learning by reducing the number of human interactions required to train deep neural networks. Instead of annotating single samples individually, humans can also label clusters, producing a higher number of annotated samples with the cost of a small label error. Our experiments show that the proposed framework requires 82% and 87% less human interactions for CIFAR-10 and EuroSAT datasets respectively when compared with the fully-supervised training while maintaining similar performance on the test set.

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Section: Related Workmentioning

confidence: 99%

Weakly Supervised Active Learning with Cluster Annotation

Perez¹,

Lebret²,

Aberer³

2018

Preprint

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“…The application of standard unsupervised ML methods, that is, clustering, to AL has been investigated in different tasks of general named entity recognition, text, and image classification (Berardo, Favero, & Neto, 2015;Dasgupta & Hsu, 2008;Nguyen & Smeulders, 2004;Shen, Zhang, Su, Zhou, & Tan, 2004;Sun et al, 2012). We previously studied the effect of clustered word and sequence representations for the models built across AL batches, on both effectiveness and annotation effort reduction.…”

Section: Unsupervised ML Approaches In Almentioning

confidence: 99%

Clinical information extraction using small data: An active learning approach based on sequence representations and word embeddings

Kholghi

Vine

Sitbon

et al. 2017

Asso for Info Science & Tech

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This article demonstrates the benefits of using sequence representations based on word embeddings to inform the seed selection and sample selection processes in an active learning pipeline for clinical information extraction. Seed selection refers to choosing an initial sample set to label to form an initial learning model. Sample selection refers to selecting informative samples to update the model at each iteration of the active learning process. Compared to supervised machine learning approaches, active learning offers the opportunity to build statistical classifiers with a reduced amount of training samples that require manual annotation. Reducing the manual annotation effort can support automating the clinical information extraction process. This is particularly beneficial in the clinical domain, where manual annotation is a time-consuming and costly task, as it requires extensive labor from clinical experts. Our empirical findings demonstrate that (a) using sequence representations along with the length of sequence for seed selection shows potential towards more effective initial models, and (b) using sequence representations for sample selection leads to significantly lower manual annotation efforts, with up to 3% and 6% fewer tokens and concepts requiring annotation, respectively, compared to state-of-the-art query strategies.

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“…[122] and Berardo, et al [123] exploited clustering in AL. Their results showed the effectiveness and stability of their approach, compared to the state-of-the-art AL approaches in text and image classification.…”

Section: Clustering and Active Learningmentioning

confidence: 99%

“…The application of the standard, unsupervised machine learning methods, i.e., clustering, to active learning, has been investigated in different tasks of general named entity recognition, text and image classification [119,120,121,122,123].…”

Section: Clustering and Active Learningmentioning

confidence: 99%

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Active Learning for Concept Extraction from Clinical Free Text

Kholghi¹

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An increasing volume of clinical free-text data, such as discharge summaries and progress reports, has been collected by hospitals and healthcare centres and stored electronically for further processing. Extracting structured clinical information from such unstructured text resources is necessary for enabling secondary usage of reports, such as reporting, reasoning and retrieving, and for further processing in down-stream eHealth workflows. However, this analysis cannot be done manually, due to the high cost incurred by qualified experts to annotate the clinical free text. A significant initial step in extracting information from clinical free text is concept extraction, which involves identifying entities of interest in the clinical domain (such as diseases, medications, and symptoms). Currently, supervised machine learning approaches effectively extract clinical concepts by building powerful statistical models. However, these approaches require a large amount of high quality, annotated train data, which is created manually by domain experts through a costly and timeconsuming process. This results in a robust active learning framework for extracting clinical concepts, using state-of-the-art, active learning approaches. The second step is to leverage clinical information resources (i.e., terminologies and clinical information extraction tools) and other machine learning approaches (i.e., semi-supervised learning, unsupervised learning, and representation learning) to develop domain-specific and generic active learning approaches. This leads to a number of novel, active learning query strategies and a seed selection approach that outperform the state-of-the-art approaches with less manual annotation effort. The last step is to validate the benefits of the developed AL-based framework in reducing the annotation cost (i.e., time) through a comprehensive user study. An AL-assisted pre-annotation scheme is also introduced, in which the learning models built across the AL process generate high quality pre-annotations to be reviewed by human annotators. This further accelerates the annotation process, by significantly reducing the number of manual annotations that must be added or corrected compared to de novo annotation.The results of this study demonstrate that AL plays an important role in reducing the manual annotation cost. The CEAL framework extracts high quality domain concepts from clinical narratives, while significantly reducing the labour cost with up to 35% less annotation time required. Additionally, AL-assisted preannotations accelerate the de novo annotation process with a further 20% less annotation time required. This thesis contributes to information extraction from clinical unstructured text resources by alleviating the burden of manual annotation.The practical significance of this research is three-fold: (1) benefitting the overall patient healthcare by facilitating downstream eHealth workflows such as supporting clinical information processing, reporting, reasoning, and efficient decision m...

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Active Learning with Clustering and Unsupervised Feature Learning

Cited by 4 publications

References 10 publications

Weakly Supervised Active Learning with Cluster Annotation

Weakly Supervised Active Learning with Cluster Annotation

Clinical information extraction using small data: An active learning approach based on sequence representations and word embeddings

Active Learning for Concept Extraction from Clinical Free Text

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