Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Bayesian aggregation of probability forecasts on categorical events
Bayesian aggregation of probability forecasts on categorical events
Partitioned logistic regression for spam filtering
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A psychological model for aggregating judgments of magnitude
SBP'11 Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction
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In order to improve forecasts, a decision-maker often combines probabilities given by various sources, such as human experts and machine learning classifiers. When few training data are available, aggregation can be improved by incorporating prior knowledge about the event being forecasted and about salient properties of the experts. To this end, we develop a generative Bayesian aggregation model for probabilistic classification. The model includes an event-specific prior, measures of individual experts' bias, calibration, accuracy, and a measure of dependence between experts. Rather than require absolute measures, we show that aggregation may be expressed in terms of relative accuracy between experts. The model results in a weighted logarithmic opinion pool (LogOps) that satisfies consistency criteria such as the external Bayesian property. We derive analytic solutions for independent and for exchangeable experts. Empirical tests demonstrate the model's use, comparing its accuracy with other aggregation methods.