2022
DOI: 10.1109/access.2022.3142724
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An Improved Extreme Learning Machine for Imbalanced Data Classification

Abstract: In the field of machine learning, Extreme Learning Machine (ELM) has been widely used in classification and regression tasks. However, like many traditional machine learning algorithms, the classification results of ELM are often not good enough when facing imbalanced data. For this reason, we proposed an extreme learning machine algorithm with output weight adjustment called OWA-ELM, which can make the decision boundary of ELM move to majority classes, and improve the classification performance of imbalanced … Show more

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Cited by 10 publications
(7 citation statements)
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“…For imbalanced learning, weighted ELMs (W-ELM) [48] were proposed by adding cost-sensitive ideas into the standard ELM algorithm to balance the data distribution. The W-ELM considers the sample weights to reinforce the impact of the minority classes and the opposite in the minority classes.…”
Section: Softvein-welm Modelmentioning
confidence: 99%
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“…For imbalanced learning, weighted ELMs (W-ELM) [48] were proposed by adding cost-sensitive ideas into the standard ELM algorithm to balance the data distribution. The W-ELM considers the sample weights to reinforce the impact of the minority classes and the opposite in the minority classes.…”
Section: Softvein-welm Modelmentioning
confidence: 99%
“…The W-ELM considers the sample weights to reinforce the impact of the minority classes and the opposite in the minority classes. Based on the R-ELM that minimizes the training error as well as the output weight norm, the optimization function of W-ELM can be represented by [48]:…”
Section: Softvein-welm Modelmentioning
confidence: 99%
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“…As a high-performance model in machine learning, ELM has achieved success in pattern recognition, computation science, and machine vision. It includes the following two merits [1][2][3][4]: fast learning speed and outstanding generalization performance. Tere is no need for ELM to tune input weight and bias, and what it only needs is to optimize the output weight by solving a least square problem.…”
Section: Introductionmentioning
confidence: 99%