An efficient alpha seeding method for optimized extreme learning machine-based feature selection algorithm

• Published in: Computers in biology and medicine Vol. 134; p. 104505
• Main Authors: Ding, Xiaojian, Yang, Fan, Jin, Sheng, Cao, Jie
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2021; Elsevier Limited
• Subjects: Algorithms; Alpha Seeding; Artificial neural networks; Bias; Computational cost; Computer applications; Datasets; Feature selection; Internal Medicine; Learning algorithms; Machine learning; Model accuracy; Optimization; Optimized extreme learning machine; Other; Quadratic programming; Seeding; Support vector machines; Computational cost; Feature selection; Alpha Seeding; Optimized extreme learning machine
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2021.104505

Embedded feature selection algorithms, such as support vector machine based recursive feature elimination (SVM-RFE), have proven to be effective for many real applications. However, due to the model selection problem, SVM-RFE naturally suffers from a heavy computational burden as well as high computational complexity. To solve these issues, this paper proposes using an optimized extreme learning machine (OELM) model instead of SVM. This model, referred to as OELM-RFE provides an efficient active set solver for training the OELM algorithm. We also present an effective alpha seeding algorithm to efficiently solve successive quadratic programming (QP) problems inherent in OELM. One of the salient characteristics of OELM-RFE is that it has only one tuning parameter: the penalty constant C. Experimental results from work on benchmark datasets show that OELM-RFE tends to have higher prediction accuracy than SVM-RFE, and requires fewer model selection efforts. In addition, the alpha seeding method works better on more datasets. •SVM-RFE naturally suffers from a heavy computational burden.•All kernel parameters in OELM-RFE are randomly assigned.•OELM-RFE outperforms SVM-RFE in terms of the generalization performance.•Alpha seeding method works better on larger number of datasets.