Minimizing risk using prediction uncertainty in neural network estimation fusion and its application to papermaking

Abstract

The paper presents Bayesian information fusion theory in the context of neural-network model combination. It shows how confidence measures can be combined with individual model estimates to minimize risk through the fusion process. The theory is illustrated through application to the real task of quality prediction in the papermaking industry. Prediction uncertainty estimates are calculated using approximate Bayesian learning. These are incorporated into model combination as confidence measures. Cost functions in the fusion center are used to control the influence of the confidence measures and improve the performance of the resultant committee.