Active learning from data streams

Xingquan Zhu, Peng Zhang, Xiaodong Lin, Yong Shi

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

96 Scopus citations

Abstract

In this paper, we address a new research problem on active learning from data streams where data volumes grow continuously and labeling all data is considered expensive and impractical. The objective is to label a small portion of stream data from which a model is derived to predict newly arrived instances as accurate as possible. In order to tackle the challenges raised by data streams' dynamic nature, we propose a classifier ensembling based active learning framework which selectively labels instances from data streams to build an accurate classifier. A Minimal Variance principle is introduced to guide instance labeling from data streams. In addition, a weight updating rule is derived to ensure that our instance labeling process can adaptively adjust to dynamic drifting concepts in the data. Experimental results on synthetic and real-world data demonstrate the performances of the proposed efforts in comparison with other simple approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages757-762
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States
Duration: Oct 28 2007Oct 31 2007

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other7th IEEE International Conference on Data Mining, ICDM 2007
Country/TerritoryUnited States
CityOmaha, NE
Period10/28/0710/31/07

All Science Journal Classification (ASJC) codes

  • General Engineering

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