Online active learning with expert advice
In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, espe...
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sg-smu-ink.sis_research-51882019-06-04T06:16:58Z Online active learning with expert advice HAO, Shuji HU, Peiying ZHAO, Peilin HOI, Steven C. H. MIAO, Chunyan In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an instance is disclosed only when it is requested by the proposed active query strategies. Our goal is to minimize the number of requests while training an online learning model without sacrificing the performance. To address this challenge, we propose a framework of active forecasters, which attempts to extend two fully supervised forecasters, Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of online active learning (OAL) with expert advice. Specifically, we proposed two OAL with expert advice algorithms, named Active Exponentially Weighted Average Forecaster (AEWAF) and active greedy forecaster (AGF), by considering the difference of expert advices. To further improve the robustness of the proposed AEWAF and AGF algorithms in the noisy scenarios (where noisy experts exist), we also proposed two robust active learning with expert advice algorithms, named Robust Active Exponentially Weighted Average Forecaster and Robust Active Greedy Forecaster. We validate the efficacy of the proposed algorithms by an extensive set of experiments in both normal scenarios (where all of experts are comparably reliable) and noisy scenarios. 2018-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4185 info:doi/10.1145/3201604 https://ink.library.smu.edu.sg/context/sis_research/article/5188/viewcontent/Online_Active_Learning_with_Expert_Advice_2018_afv.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Online learning active learning expert advice data streaming Databases and Information Systems Numerical Analysis and Scientific Computing Theory and Algorithms |
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Online learning active learning expert advice data streaming Databases and Information Systems Numerical Analysis and Scientific Computing Theory and Algorithms HAO, Shuji HU, Peiying ZHAO, Peilin HOI, Steven C. H. MIAO, Chunyan Online active learning with expert advice |
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In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an instance is disclosed only when it is requested by the proposed active query strategies. Our goal is to minimize the number of requests while training an online learning model without sacrificing the performance. To address this challenge, we propose a framework of active forecasters, which attempts to extend two fully supervised forecasters, Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of online active learning (OAL) with expert advice. Specifically, we proposed two OAL with expert advice algorithms, named Active Exponentially Weighted Average Forecaster (AEWAF) and active greedy forecaster (AGF), by considering the difference of expert advices. To further improve the robustness of the proposed AEWAF and AGF algorithms in the noisy scenarios (where noisy experts exist), we also proposed two robust active learning with expert advice algorithms, named Robust Active Exponentially Weighted Average Forecaster and Robust Active Greedy Forecaster. We validate the efficacy of the proposed algorithms by an extensive set of experiments in both normal scenarios (where all of experts are comparably reliable) and noisy scenarios. |
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text |
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HAO, Shuji HU, Peiying ZHAO, Peilin HOI, Steven C. H. MIAO, Chunyan |
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HAO, Shuji HU, Peiying ZHAO, Peilin HOI, Steven C. H. MIAO, Chunyan |
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HAO, Shuji |
title |
Online active learning with expert advice |
title_short |
Online active learning with expert advice |
title_full |
Online active learning with expert advice |
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Online active learning with expert advice |
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Online active learning with expert advice |
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online active learning with expert advice |
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Institutional Knowledge at Singapore Management University |
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2018 |
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https://ink.library.smu.edu.sg/sis_research/4185 https://ink.library.smu.edu.sg/context/sis_research/article/5188/viewcontent/Online_Active_Learning_with_Expert_Advice_2018_afv.pdf |
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