Correlated model fusion

Authors

Andrew Hoegh, Scotland Leman

Publication

Applied Stochastic Models in Business and Industry

Abstract

Model fusion methods, or more generally ensemble methods, are a useful tool for prediction. Combining predictions from a set of models smooths out biases and reduces variances of predictions from individual models, and hence, the combined predictions typically outperform those from individual models. In many algorithms, individual predictions are arithmetically averaged with equal weights. However, in the presence of correlated models, the fusion process is required to account for association between models; otherwise, the naively averaged predictions will be suboptimal. This article describes optimal model fusion principles and illustrates the potential pitfalls of naive fusion in the presence of correlated models for binary data. An efficient algorithm for correlated model fusion is detailed and applied to algorithms mining social media information to predict civil unrest.

Links

 

How is this information collected?

This collection of Montana State authored publications is collected by the Library to highlight the achievements of Montana State researchers and more fully understand the research output of the University. They use a number of resources to pull together as complete a list as possible and understand that there may be publications that are missed. If you note the omission of a current publication or want to know more about the collection and display of this information email Leila Sterman.