An improved weighted extreme learning machine for imbalanced data classification

作者：Lu, Chengbo; Ke, Haifeng^*; Zhang, Gaoyan; Mei, Ying; Xu, Huihui

来源：Memetic Computing, 2019, 11(1): 27-34.

DOI：10.1007/s12293-017-0236-3

• Imbalanced datasets
• Classification
• Weighted extreme learning machine
• Optimal weights
• Majority voting

This paper proposes an improved weighted extreme learning machine (IW-ELM) for imbalanced data classification. By incorporating voting method into weighted extreme learning machine (weighted ELM), three major steps are involved in the proposed method: training weighted ELM classifiers, eliminating unusable classifies to determine proper classifiers for voting, and finally determining the classification result based on majority voting. Simulations on many real world imbalanced datasets with various imbalance ratios have demonstrated that the proposed algorithm outperforms weighted ELM and other related classification algorithms.

• 出版日期2019-3
• 单位浙江大学城市学院; 丽水学院