Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder

Liang Yixuan

PLOS ONE, 2025, vol. 20, issue 2, 1-20

Abstract: ELM (Extreme learning machine) has drawn great attention due its high training speed and outstanding generalization performance. To solve the problem that the long training time of kernel ELM auto-encoder and the difficult setting of the weight of kernel function in the existing multi-kernel models, a multi-kernel cost-sensitive ELM method based on expectation kernel auto-encoder is proposed. Firstly, from the view of similarity, the reduced kernel auto-encoder is defined by randomly selecting the reference points from the input data; then, the reduced expectation kernel auto-encoder is designed according to the expectation kernel ELM, and the combination of random mapping and similarity mapping is realized. On this basis, two multi-kernel ELM models are designed, and the output of the classifier is converted into posterior probability. Finally, the cost-sensitive decision is realized based on the minimum risk criterion. The experimental results on the public and realistic datasets verify the effectiveness of the method.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0314851

DOI: 10.1371/journal.pone.0314851

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