Open AccessProceedings Article
Instance-Specific Bayesian Model Averaging for Classification
Shyam Visweswaran,Gregory F. Cooper +1 more
- 01 Dec 2004
- Vol. 17, pp 1449-1456
TL;DR: A lazy instance-specific algorithm called ISA is presented that performs selective model averaging over a restricted class of Bayesian networks and shows superior performance over model selection.
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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 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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Citations
•Proceedings Article
Patient-specific models for predicting the outcomes of patients with community acquired pneumonia.
Shyam Visweswaran,Gregory F. Cooper +1 more
- 01 Jan 2005
TL;DR: This study investigates two patient-specific and four population-wide machine learning methods for predicting dire outcomes in community acquired pneumonia (CAP) patients and provides support for patient- specific methods being a promising approach for making clinical predictions.
24
Distance Metric Learning for Conditional Anomaly Detection.
Michal Valko,Milos Hauskrecht +1 more
- 01 Jan 2008
TL;DR: The work presented in this paper focuses on instance-based methods for detecting conditional anomalies, which depend heavily on the distance metric that lets us identify examples in the dataset that are most critical for detecting the anomaly.
•Proceedings Article
Decision path models for patient-specific modeling of patient outcomes.
Antonio Ferreira,Gregory F. Cooper,Shyam Visweswaran +2 more
- 16 Nov 2013
TL;DR: Two patient-specific algorithms based on the decision tree paradigm are introduced that construct a decision path specific for each patient of interest compared to a single population-wide decision tree with many paths that is applicable to all patients of interest that are constructed by standard algorithms.
7
Learning to Adapt Dynamic Clinical Event Sequences with Residual Mixture of Experts
Jongmin Lee
- 01 Jan 2022
TL;DR: This work refining a one-fits-all model using a Mixture-of-Experts (MoE) architecture that augment MoE based on the prediction signal from pretrained base GRU model to provide flexible adaptation to the (limited) predictive power of the single base RNN model.
3
Patient-Specific Modeling of Medical Data
Guilherme Ribeiro,Alexandre César Muniz de Oliveira,Antonio Ferreira,Shyam Visweswaran,Gregory F. Cooper +4 more
- 20 Jul 2015
TL;DR: This work introduces two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute and applies them to predict outcomes in several datasets, including medical datasets.
1
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TL;DR: This presentation discusses the design and implementation of machine learning algorithms in Java, as well as some of the techniques used to develop and implement these algorithms.
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Bayesian Model Averaging: A Tutorial
TL;DR: Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty and provides improved out-of- sample predictive performance.
•Proceedings Article
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Usama M. Fayyad,Keki B. Irani +1 more
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TL;DR: This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued attribute into multiple intervals.
•Posted Content
On Supervised Selection of Bayesian Networks
TL;DR: In this paper, the performance of the marginal likelihood score empirically in supervised Bayesian network selection tasks by using a large number of publicly available classification data sets, and compare the results to those obtained by alternative model selection criteria, including empirical cross-validation methods, an approximation of a supervised marginal likelihood measure, and a supervised version of Dawids prequential (predictive sequential) principle.
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