SVM–ELM: Pruning of Extreme Learning Machine with Support Vector Machines for Regression

• Published in: Journal of intelligent systems 2016-10, Vol.25 (4), p.555-566
• Main Authors: Mahmood, Saif F., Marhaban, Mohammad H., Rokhani, Fakhrul Z., Samsudin, Khairulmizam, Arigbabu, Olasimbo Ayodeji
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; extreme learning machine; Investigations; Neural networks; optimal hidden node; Principal components analysis; Studies; Support vector; Support vector machines; training error
• ISSN: 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2015-0021

Extreme Learning Machine provides very competitive performance to other related classical predictive models for solving problems such as regression, clustering, and classification. An ELM possesses the advantage of faster computational time in both training and testing. However, one of the main challenges of an ELM is the selection of the optimal number of hidden nodes. This paper presents a new approach to node selection of an ELM based on a 1-norm support vector machine (SVM). In this method, the targets of SVM ∈{+1, –1} are derived using the mean or median of ELM training errors as a threshold for separating the training data, which are projected to SVM dimensions. We present an integrated architecture that exploits the sparseness in solution of SVM to prune out the inactive hidden nodes in ELM. Several experiments are conducted on real-world benchmark datasets, and the results attained attest to the efficiency of the proposed method.