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Ensemble Classification Method for Imbalanced Data Using Deep Learning

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The Ecosystem of e-Business: Technologies, Stakeholders, and Connections (WEB 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 357))

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Abstract

Nowadays various types of devices provide abundant data, and many businesses want to pinpoint what they are interested in from the data such as target marketing, fraud transaction detection. However, current classification algorithms in data mining show a poor performance when classifying imbalanced data.

To enhance the classification performance of minority class in imbalanced datasets, we present an ensemble learning method using the combination of an UnderBagging, a majority vote, and a meta classifier giving higher decision priority to the classifier that predicts a class better, basing on Deep Neural Network (DNN) as a classifier. We tested our method with two imbalanced datasets from UCI Data Repository and compared the performance of our method with four other techniques. The result showed that our method provided an improved performance on classifying minority class instances compared to the other four techniques.

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Authors and Affiliations

  1. Columbus State, Columbus, GA, 31909, USA

    Yoon Sang Lee

Authors
  1. Yoon Sang Lee
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Correspondence to Yoon Sang Lee .

Editor information

Editors and Affiliations

  1. Bentley University, Waltham, MA, USA

    Jennifer J. Xu

  2. Oregon State University, Corvallis, OR, USA

    Bin Zhu

  3. University of Utah, Salt Lake City, UT, USA

    Xiao Liu

  4. University of Illinois, Urbana-Champaign, USA

    Michael J. Shaw

  5. Georgia Institute of Technology, Atlanta, GA, USA

    Han Zhang

  6. University of Washington, Seattle, WA, USA

    Ming Fan

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Lee, Y.S. (2019). Ensemble Classification Method for Imbalanced Data Using Deep Learning. In: Xu, J., Zhu, B., Liu, X., Shaw, M., Zhang, H., Fan, M. (eds) The Ecosystem of e-Business: Technologies, Stakeholders, and Connections. WEB 2018. Lecture Notes in Business Information Processing, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-030-22784-5_16

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  • DOI: https://doi.org/10.1007/978-3-030-22784-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22783-8

  • Online ISBN: 978-3-030-22784-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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