K Means Cluster Based Undersampling Ensemble for Imbalanced Data Classification

  • Subbulaxmi S
  • et al.
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Abstract

Imbalanced data classification is a critical and challenging problem in both data mining and machine learning. Imbalanced data classification problems present in many application areas like rare medical diagnosis, risk management, fault-detection, etc. The traditional classification algorithms yield poor results in imbalanced classification problems. In this paper, K-Means cluster based undersampling ensemble algorithm is proposed to solve the imbalanced data classification problem. The proposed method combines K-Means cluster based undersampling and boosting method. The experimental results show that the proposed algorithm outperforms the other sampling ensemble algorithms of previous studies.

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Subbulaxmi, S. S., & Arumugam, G. (2020). K Means Cluster Based Undersampling Ensemble for Imbalanced Data Classification. International Journal of Engineering and Advanced Technology, 9(3), 2074–2079. https://doi.org/10.35940/ijeat.c5188.029320

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