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Effective Imbalance Learning Utilizing Informative Data

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Data Mining (AusDM 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1741))

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Abstract

The class imbalance problem that is caused by unequal data distribution usually results in poor performance, and it has attracted increasing attention in the research community. The challenge of the problem is the difficulty to extract sufficient information from the minority class. As a result, the classifier converges to a sub-optimal state. While methods based on resampling and reweighing the cost for different classes are the common strategies to address the problem, there are still numerous issues with these methods such as under- or over-sampling that may remove necessary information or introduce noise, respectively; and reweighing may result in an inappropriate cost matrix.

To address the above shortcomings, in this paper, an enhanced approach based on informative samples is proposed. In our approach, the classifier can indicate which class a sample is closer to by comparing it with boundary samples. The informative samples include the samples from both positive and negative samples located around the boundary. Finally, our experiments show that our proposed method outperforms state-of-the-art algorithms by 18% on \(F_1\) score.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Han Tai & Raymond Wong

  2. The A*STAR Centre for Frontier AI Research (CFAR), Singapore, Singapore

    Bing Li

Authors
  1. Han Tai
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  2. Raymond Wong
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  3. Bing Li
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Corresponding author

Correspondence to Raymond Wong .

Editor information

Editors and Affiliations

  1. Western Sydney University, Sydney, NSW, Australia

    Laurence A. F. Park

  2. Victoria University of Wellington, Wellington, New Zealand

    Heitor Murilo Gomes

  3. Auckland University of Technology, Auckland, New Zealand

    Maryam Doborjeh

  4. RMIT University, Melbourne, VIC, Australia

    Yee Ling Boo

  5. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  6. CSIRO Scientific Computing, Canberra, ACT, Australia

    Yanchang Zhao

  7. Australian National University, Canberra, ACT, Australia

    Graham Williams

  8. Western Sydney University, Sydney, NSW, Australia

    Simeon Simoff

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Tai, H., Wong, R., Li, B. (2022). Effective Imbalance Learning Utilizing Informative Data. In: Park, L.A.F., et al. Data Mining. AusDM 2022. Communications in Computer and Information Science, vol 1741. Springer, Singapore. https://doi.org/10.1007/978-981-19-8746-5_8

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  • DOI: https://doi.org/10.1007/978-981-19-8746-5_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8745-8

  • Online ISBN: 978-981-19-8746-5

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