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An improved weighted extreme learning machine for imbalanced data classification
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DOI：10.1007/s12293-017-0236-3.png)
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摘要
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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.
Keyword:
Imbalanced datasets
Classification
Weighted extreme learning machine
Optimal weights
Majority voting
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期刊
IF:
2.3
论文数:
459
被引数:
718
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