TY  - CONF
CY  - Oxford
ID  - orchid7
UR  - http://www.orchid.ac.uk/eprints/7/
A1  - Simpson, Edwin
A1  - Roberts, Stephen J
A1  - Smith, Arfon
A1  - Lintott, Chris
Y1  - 2011/12//
N2  - 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.
PB  - University of Oxford
TI  - Bayesian Combination of Multiple, Imperfect
Classifiers
SP  - 1
M2  - Spain
AV  - public
EP  - 8
T2  - NIPS 2011
ER  -