Instance-Specific Bayesian Model Averaging for Classification

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.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Shyam Visweswaran, Gregory F. Cooper
• Keywords: Algorithms, Instance-specific Algorithms, Lazy Instance-specific Algorithm, NIPS 2004, NIPS 2007