Published February 29, 2020 | Version v1
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K-Means Cluster Based Undersampling Ensemble for Imbalanced Data Classification

  • 1. Research Scholar, Madurai Kamaraj University, Madurai, Tamil Nadu, India,
  • 2. Professor & Head, Department of (Retd.), Computer Science, Madurai Kamaraj University, Madurai, Tamil Nadu, India
  • 1. Publisher


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 under sampling ensemble algorithm is proposed to solve the imbalanced data classification problem. The proposed method combines K-Means cluster based under sampling and boosting method. The experimental results show that the proposed algorithm outperforms the other sampling ensemble algorithms of previous studies.



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Journal article: 2249-8958 (ISSN)


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