Committee formation for reliable and accurate neural prediction in industry

This paper describes “cranking”, a new committee formation algorithm. Cranking results in accurate and reliable committee predictions, even when applied to complex industrial tasks. Prediction error estimates are used to rank a pool of models trained on bootstrap data samples. The best are then used to form a committee. This paper presents a comparison of prediction error estimates that may be used for the ranking process. In addition, it shows how the influence of poor models, due to training being unreliable, may be minimised. Experiments are carried out on an artificial task, and a real-world, decision-support task taken from the papermaking industry. In summary, this paper studies committee formation for accurate and reliable neural prediction in industrial tasks.