Active Learning Based on Crowdsourced Data

The paper proposes a crowdsourcing-based approach for annotated data acquisition and means to support Active Learning training approach. In the proposed solution, aimed at data engineers, the knowledge of the crowd serves as an oracle that is able to judge whether the given sample is informative or...

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Main Authors: Tomasz Maria Boiński, Julian Szymański, Agata Krauzewicz
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/12/1/409
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author Tomasz Maria Boiński
Julian Szymański
Agata Krauzewicz
author_facet Tomasz Maria Boiński
Julian Szymański
Agata Krauzewicz
author_sort Tomasz Maria Boiński
collection DOAJ
description The paper proposes a crowdsourcing-based approach for annotated data acquisition and means to support Active Learning training approach. In the proposed solution, aimed at data engineers, the knowledge of the crowd serves as an oracle that is able to judge whether the given sample is informative or not. The proposed solution reduces the amount of work needed to annotate large sets of data. Furthermore, it allows a perpetual increase in the trained network quality by the inclusion of new samples, gathered after network deployment. The paper also discusses means of limiting network training times, especially in the post-deployment stage, where the size of the training set can increase dramatically. This is done by the introduction of the fourth set composed of samples gather during network actual usage.
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spelling doaj.art-9c692680138943f499773f4864d5d4732023-11-23T11:12:35ZengMDPI AGApplied Sciences2076-34172022-01-0112140910.3390/app12010409Active Learning Based on Crowdsourced DataTomasz Maria Boiński0Julian Szymański1Agata Krauzewicz2Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, PolandFaculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, PolandFaculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, PolandThe paper proposes a crowdsourcing-based approach for annotated data acquisition and means to support Active Learning training approach. In the proposed solution, aimed at data engineers, the knowledge of the crowd serves as an oracle that is able to judge whether the given sample is informative or not. The proposed solution reduces the amount of work needed to annotate large sets of data. Furthermore, it allows a perpetual increase in the trained network quality by the inclusion of new samples, gathered after network deployment. The paper also discusses means of limiting network training times, especially in the post-deployment stage, where the size of the training set can increase dramatically. This is done by the introduction of the fourth set composed of samples gather during network actual usage.https://www.mdpi.com/2076-3417/12/1/409active learningsample assessmentcrowdsourcing
spellingShingle Tomasz Maria Boiński
Julian Szymański
Agata Krauzewicz
Active Learning Based on Crowdsourced Data
Applied Sciences
active learning
sample assessment
crowdsourcing
title Active Learning Based on Crowdsourced Data
title_full Active Learning Based on Crowdsourced Data
title_fullStr Active Learning Based on Crowdsourced Data
title_full_unstemmed Active Learning Based on Crowdsourced Data
title_short Active Learning Based on Crowdsourced Data
title_sort active learning based on crowdsourced data
topic active learning
sample assessment
crowdsourcing
url https://www.mdpi.com/2076-3417/12/1/409
work_keys_str_mv AT tomaszmariaboinski activelearningbasedoncrowdsourceddata
AT julianszymanski activelearningbasedoncrowdsourceddata
AT agatakrauzewicz activelearningbasedoncrowdsourceddata