K-Means Cluster Based Undersampling Ensemble for Imbalanced Data Classification
| Main Authors: | S. Santha Subbulaxmi, G. Arumugam |
|---|---|
| Other Authors: | Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) |
| Format: | Article Journal |
| Bahasa: | eng |
| Terbitan: |
, 2020
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/5577385 |
Daftar Isi:
- 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.