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Feedback-driven multiclass active learning for data streams

Authors:

Zhengzhang Chen,

Alok ChoudharyAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1311 - 1320

https://doi.org/10.1145/2505515.2505528

Published: 27 October 2013 Publication History

Abstract

Active learning is a promising way to efficiently build up training sets with minimal supervision. Most existing methods consider the learning problem in a pool-based setting. However, in a lot of real-world learning tasks, such as crowdsourcing, the unlabeled samples, arrive sequentially in the form of continuous rapid streams. Thus, preparing a pool of unlabeled data for active learning is impractical. Moreover, performing exhaustive search in a data pool is expensive, and therefore unsuitable for supporting on-the-fly interactive learning in large scale data. In this paper, we present a systematic framework for stream-based multi-class active learning. Following the reinforcement learning framework, we propose a feedback-driven active learning approach by adaptively combining different criteria in a time-varying manner. Our method is able to balance exploration and exploitation during the learning process. Extensive evaluation on various benchmark and real-world datasets demonstrates the superiority of our framework over existing methods.

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Cited By

Salehi SSchmeink A(2024)Data-Centric Green Artificial Intelligence: A SurveyIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33152725:5(1973-1989)Online publication date: May-2024
https://doi.org/10.1109/TAI.2023.3315272
Lv QDong M(2023)An adaptive active learning algorithm with informativeness and representativenessIntelligent Data Analysis10.3233/IDA-21641827:1(199-222)Online publication date: 30-Jan-2023
https://doi.org/10.3233/IDA-216418
Khan AHaq IHussain TMuhammad KHijji MSajjad MDe Albuquerque VBaik S(2022)PMAL: A Proxy Model Active Learning Approach for Vision Based Industrial ApplicationsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/353493218:2s(1-18)Online publication date: 6-Oct-2022
https://dl.acm.org/doi/10.1145/3534932
Show More Cited By

Index Terms

Feedback-driven multiclass active learning for data streams
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 ACM.

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Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Salehi SSchmeink A(2024)Data-Centric Green Artificial Intelligence: A SurveyIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.33152725:5(1973-1989)Online publication date: May-2024
https://doi.org/10.1109/TAI.2023.3315272
Lv QDong M(2023)An adaptive active learning algorithm with informativeness and representativenessIntelligent Data Analysis10.3233/IDA-21641827:1(199-222)Online publication date: 30-Jan-2023
https://doi.org/10.3233/IDA-216418
Khan AHaq IHussain TMuhammad KHijji MSajjad MDe Albuquerque VBaik S(2022)PMAL: A Proxy Model Active Learning Approach for Vision Based Industrial ApplicationsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/353493218:2s(1-18)Online publication date: 6-Oct-2022
https://dl.acm.org/doi/10.1145/3534932
Shahraki AAbbasi MTaherkordi AJurcut A(2022)Active Learning for Network Traffic Classification: A Technical StudyIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2021.31190628:1(422-439)Online publication date: Mar-2022
https://doi.org/10.1109/TCCN.2021.3119062
Kim YKim W(2022)Uncertainty Assessment-Based Active Learning for Reliable Fire Detection SystemsIEEE Access10.1109/ACCESS.2022.319085210(74722-74732)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3190852
Barata RLeite MPacheco RSampaio MAscensão JBizarro PCalinescu ASzpruch L(2021)Active learning for imbalanced data under cold startProceedings of the Second ACM International Conference on AI in Finance10.1145/3490354.3494423(1-9)Online publication date: 3-Nov-2021
https://dl.acm.org/doi/10.1145/3490354.3494423
Balasubramanian TEstrada T(2021)Crowdlearning: A framework for collaborative and personalized learning2016 IEEE Frontiers in Education Conference (FIE)10.1109/FIE.2016.7757355(1-9)Online publication date: 11-Mar-2021
https://dl.acm.org/doi/10.1109/FIE.2016.7757355
Du TShen GHuang ZYu YWu D(2020)Automatic traceability link recovery via active learningFrontiers of Information Technology & Electronic Engineering10.1631/FITEE.190022221:8(1217-1225)Online publication date: 4-Jul-2020
https://doi.org/10.1631/FITEE.1900222
Khamesi AShin ESilvestri S(2020)Machine Learning in the Wild: The Case of User-Centered Learning in Cyber Physical Systems2020 International Conference on COMmunication Systems & NETworkS (COMSNETS)10.1109/COMSNETS48256.2020.9027329(275-281)Online publication date: Jan-2020
https://doi.org/10.1109/COMSNETS48256.2020.9027329
Tegen ADavidsson PPersson J(2020)Activity recognition through interactive machine learning in a dynamic sensor settingPersonal and Ubiquitous Computing10.1007/s00779-020-01414-228:1(273-286)Online publication date: 9-Jun-2020
https://dl.acm.org/doi/10.1007/s00779-020-01414-2
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