Combining models across algorithms and samples for improved results

Multiple approaches have been developed for improving predictive performance of a system by creating and combining various learned models. There are two main approaches to creating model ensembles. The first is to create a set of learned models by applying an algorithm repeatedly to different training sample data, while the second approach applies various learning algorithms to the same sample data. The predictions of the models are then combined according to a voting scheme. This paper presents a method for combining models that was developed using numerous samples, modeling algorithms, and modelers and compares it with the alternate approaches. The results of the model combination methods are evaluated with respect to sensitivity and false alarm rates and are then compared against other approaches.