Confidence-weighted extreme learning machine for regression problems

Abstract Based on Gaussian margin machine (GMM) and extreme learning machine (ELM), confidence-weighted ELM (CW-ELM) is proposed to provide point forecasts and confidence intervals. CW-ELM maintains a multivariate normal distribution over the output weight vector. It is applied to seek the least informative distribution from those that keep the targets within the forecast confidence intervals. For simplicity, the covariance matrix is assumed to be diagonal. The simplified problem of CW-ELM is approximately solved by using Leave-One-Out-Incremental ELM (LOO-IELM) and the interior point method. Our experimental results on both synthetic and real-world regression datasets demonstrate that CW-ELM has better performance than Bayesian ELM and Gaussian process regression.