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Budget-Driven Big Data Classification

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Advances in Artificial Intelligence (Canadian AI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9091))

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Abstract

A practical large-scale data classification approach is presented in this paper. By exploiting online learning framework, our approach learns a set of competing one-class Support Vector Machine models, one for each data class. The presented approach enjoys three budget-driven features: 1) it is capable of handling classification when data cannot fit in memory; 2) both training and labeling process is user controllable; 3) the classifiers can easily adapt to changes in dynamic data with minimal computational cost. Compared with the most popular big data classification tool, LibLinear, our approach is shown to be competent at processing extreme large data, while consuming a fractional of memory and time.

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Authors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, St. John’s, Newfoundland, NL, A1B 3X5, Canada

    Yiming Qian, Hao Yuan & Minglun Gong

Authors
  1. Yiming Qian
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  2. Hao Yuan
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  3. Minglun Gong
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Corresponding author

Correspondence to Minglun Gong .

Editor information

Editors and Affiliations

  1. University of Alberta, Edmonton, Canada

    Denilson Barbosa

  2. Dalhousie University, Halifax, Canada

    Evangelos Milios

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Qian, Y., Yuan, H., Gong, M. (2015). Budget-Driven Big Data Classification. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-18356-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18355-8

  • Online ISBN: 978-3-319-18356-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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