Journal article Open Access

K-Means Cluster Based Undersampling Ensemble for Imbalanced Data Classification

S. Santha Subbulaxmi; G. Arumugam

Sponsor(s)
Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)

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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