Active semi-supervised learning for biological data classification

Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations...

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Published inPloS one Vol. 15; no. 8; p. e0237428
Main Authors Camargo, Guilherme, Bugatti, Pedro H, Saito, Priscila T M
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 19.08.2020
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Abstract Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting. A smaller number of more informative samples previously selected (by the active learning strategy) and labeled by a specialist can propagate the labels to a set of unlabeled data (through the semi-supervised one). However, most of the literature works neglect the need for interactive response times that can be required by certain real applications. We propose a more effective and efficient active semi-supervised learning framework, including a new active learning method. An extensive experimental evaluation was performed in the biological context (using the ALL-AML, Escherichia coli and PlantLeaves II datasets), comparing our proposals with state-of-the-art literature works and different supervised (SVM, RF, OPF) and semi-supervised (YATSI-SVM, YATSI-RF and YATSI-OPF) classifiers. From the obtained results, we can observe the benefits of our framework, which allows the classifier to achieve higher accuracies more quickly with a reduced number of annotated samples. Moreover, the selection criterion adopted by our active learning method, based on diversity and uncertainty, enables the prioritization of the most informative boundary samples for the learning process. We obtained a gain of up to 20% against other learning techniques. The active semi-supervised learning approaches presented a better trade-off (accuracies and competitive and viable computational times) when compared with the active supervised learning ones.
AbstractList Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in disproportion to the scarcity of labeled data. Another important issue is related to the trade-off between the difficulty in obtaining annotations provided by a specialist and the need for a significant amount of annotated data to obtain a robust classifier. In this context, active learning techniques jointly with semi-supervised learning are interesting. A smaller number of more informative samples previously selected (by the active learning strategy) and labeled by a specialist can propagate the labels to a set of unlabeled data (through the semi-supervised one). However, most of the literature works neglect the need for interactive response times that can be required by certain real applications. We propose a more effective and efficient active semi-supervised learning framework, including a new active learning method. An extensive experimental evaluation was performed in the biological context (using the ALL-AML, Escherichia coli and PlantLeaves II datasets), comparing our proposals with state-of-the-art literature works and different supervised (SVM, RF, OPF) and semi-supervised (YATSI-SVM, YATSI-RF and YATSI-OPF) classifiers. From the obtained results, we can observe the benefits of our framework, which allows the classifier to achieve higher accuracies more quickly with a reduced number of annotated samples. Moreover, the selection criterion adopted by our active learning method, based on diversity and uncertainty, enables the prioritization of the most informative boundary samples for the learning process. We obtained a gain of up to 20% against other learning techniques. The active semi-supervised learning approaches presented a better trade-off (accuracies and competitive and viable computational times) when compared with the active supervised learning ones.
Audience Academic
Author Camargo, Guilherme
Bugatti, Pedro H
Saito, Priscila T M
AuthorAffiliation Korea National University of Transportation, KOREA, REPUBLIC OF
1 Department of Computing, Federal University of Technology - Paraná, Cornélio Procópio, PR, Brazil
2 Institute of Computing, University of Campinas, Campinas, SP, Brazil
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  organization: Department of Computing, Federal University of Technology - Paraná, Cornélio Procópio, PR, Brazil
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  givenname: Pedro H
  surname: Bugatti
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  orcidid: 0000-0002-4870-4766
  surname: Saito
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  organization: Institute of Computing, University of Campinas, Campinas, SP, Brazil
BackLink https://www.ncbi.nlm.nih.gov/pubmed/32813738$$D View this record in MEDLINE/PubMed
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Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: currently, one of the authors (Priscila T. M. Saito) of the present work is a member of the PLOS ONE Editorial Board, acting as an academic editor. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
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Snippet Due to datasets have continuously grown, efforts have been performed in the attempt to solve the problem related to the large amount of unlabeled data in...
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SubjectTerms Active learning
Annotations
Biological research
Biology and Life Sciences
Classification
Classifiers
Computer and Information Sciences
Computer applications
Context
Data Management - methods
Datasets
E coli
Learning
Machine learning
Medicine and Health Sciences
Methods
Research and Analysis Methods
Semi-supervised learning
Social Sciences
Supervised Machine Learning
Technology application
Tradeoffs
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Title Active semi-supervised learning for biological data classification
URI https://www.ncbi.nlm.nih.gov/pubmed/32813738
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https://pubmed.ncbi.nlm.nih.gov/PMC7437865
https://doaj.org/article/3448c0431812440f8108c504ee50bc3e
http://dx.doi.org/10.1371/journal.pone.0237428
Volume 15
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