Dynamic Bayesian Combination of Multiple Imperfect Classifiers

Edwin Simpson, Stephen Roberts, Ioannis Psorakis, Arfon Smith

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

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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 chapter 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. Finally we present a dynamic Bayesian classifier combination approach and investigate the changes in base classifier performance over time.
Original languageEnglish
Title of host publicationDecision Making and Imperfection
EditorsTatiana Guy, Miroslav Karny, David Wolpert
Chapter1
Pages1-35
Number of pages36
Volume474
ISBN (Electronic)978-3-642-36406-8
DOIs
Publication statusPublished - 9 Feb 2013

Publication series

NameStudies in Computational Intelligence
PublisherSpringer Verlag
ISSN (Print)1860-949X

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