Elastic-net based robust extreme learning machine for one-class classification

The one-class extreme learning machine (OC-ELM) builds a classification model by learning samples of the known class to detect abnormal samples, and has the advantages of high learning speed and good generalization performance. However, OC-ELM with the square loss function is sensitive to outliers, which leads to poor robustness. To address this issue, an elastic-net based robust extreme learning machine for one-class classification (ER-OCELM) is proposed to achieve both excellent robustness and comparable sparsity. Correntropy loss function is utilized to limit the negative effects of outliers due to its property of bounded. With an elastic-net regularizer, automatic variable selection and continuous shrinkage can be performed simultaneously, and groups of correlated variables can be selected to make the model sparse. The corresponding optimization can be transformed into the linear equation system. We employ an it-erative reweighted algorithm to obtain the optimal solution. In each iteration, the solution form of ER-OCELM is similar to that of ELM. Experiments on artificial and benchmark datasets verify that ER-OCELM has superior robustness and sparsity compared to many state-of-the-art methods. (c) 2023 Elsevier B.V. All rights reserved.