Feedback-driven multiclass active learning for data streams

Yu Cheng, Zhengzhang Chen, Lu Liu, Jiang Wang, Ankit Agrawal, Alok Choudhary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

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 crowd-sourcing, 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.

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages1311-1320
Number of pages10
DOIs
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

Keywords

  • Active learning
  • Adaptive criteria
  • Reinforcement learning
  • Stream data mining

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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