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,...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2025-02, Vol.20 (2), p.e0314851
Main Author: Yixuan, Liang
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Biology and Life Sciences
Classification
Coders
Computer and Information Sciences
Conditional probability
Costs
Earth Sciences
Humans
Kernel functions
Machine Learning
Mapping
Medicine and Health Sciences
Neural networks
Physical Sciences
Research and Analysis Methods
Similarity
Similarity measures
Social Sciences
Training
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0314851