Local Feature Extraction based KELM for Face Recognition

Ahuja, Bhawna; Vishwakarma, Virendra P.

doi:10.1109/ic3.2019.8844888

Cited by 8 publications

(1 citation statement)

References 19 publications

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“…Therefore, ELM has the advantages of fast learning speed and good generalization performance. It has been widely used in data classification problems in various fields, such as face recognition [11][12], military [13], image processing [14][15] and medical diagnosis [16][17] and etc. However, just like traditional machine learning algorithms, ELM was not designed to classify imbalanced data, which leads to its poor classification effect on imbalanced data [18].…”

Section: Introductionmentioning

confidence: 99%

An Improved Extreme Learning Machine for Imbalanced Data Classification

Zhang

Qin

2022

IEEE Access

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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 Δ to the connection weights between hidden layer neurons and minority output neurons in ELM, so that the output value of minority output neurons 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, OWA-ELM algorithm has achieved 9 times best and 4 times suboptimal results on G-mean. In F-measure, 13 best and 1 second best results were obtained. Therefore, the OWA-ELM algorithm is effective to deal with imbalanced data classification.

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