title: Bayesian Combination of Multiple, Imperfect  Classifiers
creator: Simpson, Edwin
creator: Roberts, Stephen J
creator: Smith, Arfon
creator: Lintott, Chris
subject: Machine Learning
subject: Applications
subject: Incentive Engineering
description: 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. We apply the  approach to real data from a large citizen science project, Galaxy Zoo Supernovae,  and show that our method far outperforms other established approaches to imperfect  decision combination. We go on to analyse the putative community structure  of the decision makers, based on their inferred decision making strategies, and  show that natural groupings are formed.
publisher: University of Oxford
date: 2011-12
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
identifier: http://www.orchid.ac.uk/eprints/7/1/vbibcc_workshop.pdf
format: application/pdf
identifier: http://www.orchid.ac.uk/eprints/7/4/vbibcc_workshop.pdf
identifier:   Simpson, Edwin and Roberts, Stephen J and Smith, Arfon and Lintott, Chris  (2011) Bayesian Combination of Multiple, Imperfect Classifiers.  In: NIPS 2011, December 2011, Spain.     
relation: http://www.orchid.ac.uk/eprints/7/