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

Computer Engineering Department, Cairo University, Giza 12613, Egypt
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Entropy 2019, 21(7), 651; https://doi.org/10.3390/e21070651
Received: 31 May 2019 / Revised: 28 June 2019 / Accepted: 28 June 2019 / Published: 1 July 2019
(This article belongs to the Special Issue Information Theoretic Learning and Kernel Methods)
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Abstract

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. View Full-Text
Keywords: active learning; exploration-exploitation; regression; optimization; mutual information; Kullback–Leibler divergence; entropy; query synthesis; demand learning; exploration-exploitation; sequential decision problems active learning; exploration-exploitation; regression; optimization; mutual information; Kullback–Leibler divergence; entropy; query synthesis; demand learning; exploration-exploitation; sequential decision problems
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Elreedy, D.; F. Atiya, A.; I. Shaheen, S. A Novel Active Learning Regression Framework for Balancing the Exploration-Exploitation Trade-Off. Entropy 2019, 21, 651.

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