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

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,...

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Main Author: Liang Yixuan
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0314851
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author Liang Yixuan
author_facet Liang Yixuan
author_sort Liang Yixuan
collection DOAJ
description 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.
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institution Kabale University
issn 1932-6203
language English
publishDate 2025-01-01
publisher Public Library of Science (PLoS)
record_format Article
series PLoS ONE
spelling doaj-art-e01f2f3f23bc44f78a4cb496398581372025-08-20T03:48:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031485110.1371/journal.pone.0314851Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.Liang YixuanELM (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.https://doi.org/10.1371/journal.pone.0314851
spellingShingle Liang Yixuan
Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.
PLoS ONE
title Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.
title_full Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.
title_fullStr Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.
title_full_unstemmed Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.
title_short Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.
title_sort cost sensitive multi kernel elm based on reduced expectation kernel auto encoder
url https://doi.org/10.1371/journal.pone.0314851
work_keys_str_mv AT liangyixuan costsensitivemultikernelelmbasedonreducedexpectationkernelautoencoder