@article{Jin2026, 
author = {Xiang Jin and Guolin Yu and Jun Ma},
title = {Robust twin extreme learning machine with adaptive fractional loss},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {4},
pages = {9228-9259},
keywords = {twin extreme learning machine, adaptive fractional loss, robustness, noise insensitivity},
url = {https://www.sciopen.com/article/10.3934/math.2026381},
doi = {10.3934/math.2026381},
abstract = {The twin extreme learning machine (TELM), based on the hinge-loss function, demonstrates significant potential for pattern classification tasks. However, the hinge-loss function, which minimizes the shortest distance between sets, results in classifiers that are sensitive to noise and unstable in the presence of overfitting. To enhance TELM's performance, a novel learning framework, termed adaptive fractional loss TELM (AFTELM), is proposed. This framework incorporates an adaptive fractional loss (AF-loss) function, offering improved robustness to noise compared to TELM with hinge loss. A theoretical analysis is provided to examine the noise insensitivity of AFTELM. The concave-convex procedure (CCCP) is employed for efficient optimization. Extensive experiments on benchmark datasets validate the superior performance of AFTELM, demonstrating its robustness to noise and enhanced classification ability.}
}