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

• 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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0314851

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.