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Cluster-Based Under-Sampling Using Farthest Neighbour Technique for Imbalanced Datasets

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Innovations in Bio-Inspired Computing and Applications (IBICA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1180))

Abstract

In domain of data mining, learning from imbalanced class distribution datasets is a challenging problem for conventional classifiers. The class imbalance exists when the number of samples of one class is much lesser than the ones of the other classes. In real-world classification problems, data samples often have unequal class distribution. This problem is represented as a class imbalance problem. However, many solutions have been proposed in the literature to improve classifier performance. But recent works entitlement that imbalanced dataset is not a problem in itself. The degradation of classifier performance is also linked with many factors like small sample size, sample overlapping, class disjunct and many more. In this work, we proposed cluster-based under-sampling based on farthest neighbors. The majority class samples are selected based on the average distance to all minority class samples in the cluster are farthest. The experimental results show that our cluster-based under-sampling approach outperform with existing techniques in the previous studies.

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Notes

  1. 1.

    https://sci2s.ugr.es/keel/imbalanced.php.

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Acknowledgment

This Research is funded by Anumit Academy’s Research and Innovation Network (AARIN), India. The Author Would Like to Thank AARIN, India, a Research Network for Supporting The Project Through its Financial Assistance.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, India

    G. Rekha

  2. School of Computing Science and Engineering, Vellore Institute of Technology, Chennai Campus, Chennai, 600127, Tamilnadu, India

    Amit Kumar Tyagi

Authors
  1. G. Rekha
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  2. Amit Kumar Tyagi
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Corresponding authors

Correspondence to G. Rekha or Amit Kumar Tyagi .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Utkal University, Bhubaneswar, Odisha, India

    Mrutyunjaya Panda

  3. Department of Electronics Engineering, GIET University, Gunupur, Odisha, India

    Subhrajit Pradhan

  4. Area of Project Engineering, University of Cordoba, Córdoba, Córdoba, Spain

    Laura Garcia-Hernandez

  5. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Rekha, G., Tyagi, A.K. (2021). Cluster-Based Under-Sampling Using Farthest Neighbour Technique for Imbalanced Datasets. In: Abraham, A., Panda, M., Pradhan, S., Garcia-Hernandez, L., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2019. Advances in Intelligent Systems and Computing, vol 1180. Springer, Cham. https://doi.org/10.1007/978-3-030-49339-4_5

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