Incremental and sequence learning algorithms for weighted regularized extreme learning machines

The adoption of weighted regularized extreme learning machines (WR-ELMs) has been recognized as an effective approach to addressing class imbalance by differentially weighting sample classes. Traditional batch learning methodologies, however, falter due to their inefficiency in adapting to network restructuring and their inability to process streaming data. By introducing incremental and sequential learning, the incremental weighted regularized extreme learning machine (IWR-ELM) and the online weighted regularized extreme learning machine (OWR-ELM) are proposed in this paper to enhance WR-ELM’s flexibility and responsiveness. Specifically, the IWR-ELM facilitates optimal hidden layer node selection, thereby enhancing model adaptability without necessitating full retraining. Conversely, the OWR-ELM is engineered for real-time data stream processing, enabling continuous learning from new data segments without retaining outdated information. We also address the concurrent challenges of concept drift and class imbalance by presenting an enhanced online weighted regularized extreme learning machine, which incorporates enhancement factors to elevate the significance of recent data. Finally, the competitiveness of our proposed algorithm is demonstrated in terms of its training time and performance through extensive experiments conducted on class-imbalanced datasets. Our comprehensive evaluations on diverse class-imbalanced datasets affirm the superior efficiency and performance of our proposed solutions in terms of training speed and accuracy.