Meta-constrained extreme learning machines: a novel approach for classification problems

Abstract

Extreme learning machine (ELM) is a fast and generalizable learning model widely used in classification and regression tasks. Meta-ELM improves the performance of the ELM by combining multiple base ELMs, each trained on different data partitions. These base models were then used as hidden nodes for the parent ELM, forming a hierarchical architecture. Constrained ELM (CELM) improves upon standard ELM by replacing the random input weight assignment with weights derived from combinations of selected data samples. There are five known variants of CELM, each based on different combination strategies. This study proposes a novel model, the CELM, which integrates the Meta-ELM framework with the structured weight assignment of the CELM. Rather than using random weights, it constructs input weights from representative data combinations, thereby improving the learning stability and performance. The model was evaluated on 10 benchmark classification datasets, including adult census income, breast cancer, diabetes, dry bean, fashion MNIST, heart disease, raisin, rice, wine, and Modified National Institute of Standards and Technology. The results showed consistent improvements in accuracy, precision, recall, and F1 score. The Meta-difference extreme learning machine (DELM) variant of the proposed model outperforms standard ELM and Meta-ELM, while showing competitive results compared with various state-of-the-art models such as support vector machines, random forest, XGBoost, LightGBM, and CatBoost. These findings confirm the robustness of the proposed Meta-DELM, achieving an accuracy of 87.29% across all datasets.