Dynamic Bayesian Combination of Multiple Imperfect Classifiers

Understand

Classifier combination methods need to make best use of the outputs of multiple, imperfect classifiers to enable higher accuracy classifications.

• In many situations, such as when human decisions need to be combined, the base decisions can vary enormously in reliability.
• A Bayesian approach to such uncertain combination allows us to infer the differences in performance between individuals and to incorporate any available prior knowledge about their abilities when training data is sparse.
• In this paper we explore Bayesian classifier combination, using the computationally efficient framework of variational Bayesian inference.