Improving Predictions Using Linear Combination Of Multiple Extreme Learning Machines

Abstract

This work presents several effective approaches for linear combination of multiple artificial neural networks based on the extreme learning machine (ELM) algorithm. Given a learning task, a large set of neural networks are firstly trained by ELM. Then, these trained machines are efficiently ranked and the useless models are effectively discarded in order to provide an ensemble system with better generalization performance. The ensemble system is constructed using an automatic and fast forward model selection by minimizing the leave-one-out error, without user intervention. Experiments on an artificial regression dataset and three real-world engineering problems are discussed. According to the obtained results, the weighted linear combination of ELMs improves predictions by exploiting model diversity in the ensemble system with fast learning speed.

DOI: http://dx.doi.org/10.5755/j01.itc.42.1.1667