@article{Ma2026, 
author = {Yanrong Ma and Jun Ma and Bao Ma},
title = {Robust average-weighted twin extreme learning machine for pattern classification},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {4},
pages = {10100-10132},
keywords = {average weight, pre-selection, classification, robust, manifold regularization},
url = {https://www.sciopen.com/article/10.3934/math.2026417},
doi = {10.3934/math.2026417},
abstract = {This paper proposes a novel robust twin extreme learning machine (RTELM) for binary classification. To enhance its performance and robustness, we introduce two key techniques: (1) An average weight technique that assigns larger weights to data points near the class center and smaller weights to those near the boundary, leveraging the intra-class distribution; and (2) an improved pre-selection point technique that selects only the top-   u sorted data points to mitigate the impact of noise and outliers. Furthermore, we extend RTELM by incorporating a manifold regularization term, resulting in the Lap-RTELM framework, which enhances data discriminability. Extensive experiments validate that both RTELM and Lap-RTELM achieve superior classification performance, robustness, and stability compared to traditional methods.}
}