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A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off

Recently, active learning is considered a promising approach for data acquisition due to the significant cost of the data labeling process in many real world applications, such as natural language processing and image processing. Most active learning methods are merely designed to enhance the learni...

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Detalles Bibliográficos
Autores principales: Elreedy, Dina, F. Atiya, Amir, I. Shaheen, Samir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515147/
https://www.ncbi.nlm.nih.gov/pubmed/33267365
http://dx.doi.org/10.3390/e21070651
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author Elreedy, Dina
F. Atiya, Amir
I. Shaheen, Samir
author_facet Elreedy, Dina
F. Atiya, Amir
I. Shaheen, Samir
author_sort Elreedy, Dina
collection PubMed
description Recently, active learning is considered a promising approach for data acquisition due to the significant cost of the data labeling process in many real world applications, such as natural language processing and image processing. Most active learning methods are merely designed to enhance the learning model accuracy. However, the model accuracy may not be the primary goal and there could be other domain-specific objectives to be optimized. In this work, we develop a novel active learning framework that aims to solve a general class of optimization problems. The proposed framework mainly targets the optimization problems exposed to the exploration-exploitation trade-off. The active learning framework is comprehensive, it includes exploration-based, exploitation-based and balancing strategies that seek to achieve the balance between exploration and exploitation. The paper mainly considers regression tasks, as they are under-researched in the active learning field compared to classification tasks. Furthermore, in this work, we investigate the different active querying approaches—pool-based and the query synthesis—and compare them. We apply the proposed framework to the problem of learning the price-demand function, an application that is important in optimal product pricing and dynamic (or time-varying) pricing. In our experiments, we provide a comparative study including the proposed framework strategies and some other baselines. The accomplished results demonstrate a significant performance for the proposed methods.
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spelling pubmed-75151472020-11-09 A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off Elreedy, Dina F. Atiya, Amir I. Shaheen, Samir Entropy (Basel) Article Recently, active learning is considered a promising approach for data acquisition due to the significant cost of the data labeling process in many real world applications, such as natural language processing and image processing. Most active learning methods are merely designed to enhance the learning model accuracy. However, the model accuracy may not be the primary goal and there could be other domain-specific objectives to be optimized. In this work, we develop a novel active learning framework that aims to solve a general class of optimization problems. The proposed framework mainly targets the optimization problems exposed to the exploration-exploitation trade-off. The active learning framework is comprehensive, it includes exploration-based, exploitation-based and balancing strategies that seek to achieve the balance between exploration and exploitation. The paper mainly considers regression tasks, as they are under-researched in the active learning field compared to classification tasks. Furthermore, in this work, we investigate the different active querying approaches—pool-based and the query synthesis—and compare them. We apply the proposed framework to the problem of learning the price-demand function, an application that is important in optimal product pricing and dynamic (or time-varying) pricing. In our experiments, we provide a comparative study including the proposed framework strategies and some other baselines. The accomplished results demonstrate a significant performance for the proposed methods. MDPI 2019-07-01 /pmc/articles/PMC7515147/ /pubmed/33267365 http://dx.doi.org/10.3390/e21070651 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Elreedy, Dina
F. Atiya, Amir
I. Shaheen, Samir
A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off
title A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off
title_full A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off
title_fullStr A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off
title_full_unstemmed A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off
title_short A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off
title_sort novel active learning regression framework for balancing the exploration-exploitation trade-off
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515147/
https://www.ncbi.nlm.nih.gov/pubmed/33267365
http://dx.doi.org/10.3390/e21070651
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