Instance-specific Bayesian model averaging for classification
Visweswaran S, Cooper GF. Instance-specific Bayesian model averaging for classification. In: Proceedings of the Neural Information Processing Systems Conference (NIPS) (2005) 1449-1456.
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for learning instance-specific models from data that are optimized to predict well for a particular instance. Based on this framework, we present a lazy instance-specific algorithm called ISA that performs selective model averaging over a restricted class of Bayesian networks. On experimental evaluation, this algorithm shows superior performance over model selection. We intend to apply such instance-specific algorithms to improve the performance of patient-specific predictive models induced from medical data.
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