creators_name: Simpson, Edwin
creators_name: Roberts, Stephen J
creators_name: Smith, Arfon
creators_name: Lintott, Chris
type: conference_item
datestamp: 2011-11-04 10:41:56
lastmod: 2012-08-07 12:24:53
metadata_visibility: show
title: Bayesian Combination of Multiple, Imperfect
Classifiers
ispublished: pub
subjects: ML
subjects: app
subjects: ie
full_text_status: public
monograph_type: working_paper
pres_type: paper
abstract: 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.
date: 2011-12
date_type: published
publisher: University of Oxford
place_of_pub: Oxford
pagerange: 1-8
pages: 8
event_title: NIPS 2011
event_location: Spain
event_dates: December 2011
event_type: conference
institution: University of Oxford
refereed: TRUE
citation:   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.     
document_url: http://www.orchid.ac.uk/eprints/7/1/vbibcc_workshop.pdf
document_url: http://www.orchid.ac.uk/eprints/7/4/vbibcc_workshop.pdf