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An Improved Extreme Learning Machine for Imbalanced Data Classification
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DOI：10.1109/ACCESS.2022.3142724.png)
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Abstract
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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 data. Specifically, in ELM, we add a reasonable increment 1 to the connection weights between hidden layer neurons and minority output neurons, so that the output value of minority output neuron increases. Finally, the classification accuracy of the minority class samples can be improved without significantly affecting the classification of the majority class samples. The performance of OWA-ELM was compared with ELM, WELM, CS-ELM and CCR-ELM. In the experiments on 22 data sets, the OWA-ELM algorithm has achieved 9 times optimal and 4 times suboptimal results on G-mean. In F-measure, 13 times optimal and one suboptimal results were obtained. Therefore, the OWA-ELM algorithm is effective to deal with imbalanced data classification.
Keywords:
Extreme learning machine
imbalanced data
classification
output weights adjustment
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