Instance-Specific Bayesian Model Averaging for Classification
Abstract
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 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.
Cite
Text
Visweswaran and Cooper. "Instance-Specific Bayesian Model Averaging for Classification." Neural Information Processing Systems, 2004.Markdown
[Visweswaran and Cooper. "Instance-Specific Bayesian Model Averaging for Classification." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/visweswaran2004neurips-instancespecific/)BibTeX
@inproceedings{visweswaran2004neurips-instancespecific,
title = {{Instance-Specific Bayesian Model Averaging for Classification}},
author = {Visweswaran, Shyam and Cooper, Gregory F.},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1449-1456},
url = {https://mlanthology.org/neurips/2004/visweswaran2004neurips-instancespecific/}
}