Dynamic Bayesian Combination of Multiple Imperfect Classifiers

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. Finally we present a dynamic Bayesian classifier combination approach and investigate the changes in base classifier performance over time.Comment: 35 pages, 12 figure

Dynamic probabilistic CCA for analysis of affective behaviour

Fusing multiple continuous expert annotations is a crucial problem in machine learning and computer vision, particularly when dealing with uncertain and subjective tasks related to affective behaviour. Inspired by the concept of inferring shared and individual latent spaces in probabilistic CCA (PCCA), we firstly propose a novel, generative model which discovers temporal dependencies on the shared/individual spaces (DPCCA). In order to accommodate for temporal lags which are prominent amongst continuous annotations, we further introduce a latent warping process. We show that the resulting model (DPCTW) (i) can be used as a unifying framework for solving the problems of temporal alignment and fusion of multiple annotations in time, and (ii) that by incorporating dynamics, modelling annotation/sequence specific biases, noise estimation and time warping, DPCTW outperforms state-of-the-art methods for both the aggregation of multiple, yet imperfect expert annotations as well as the alignment of affective behavior