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Bayesian Combination of Multiple Plasma Glucose Predictors

Ståhl, Fredrik LU ; Johansson, Rolf LU and Renard, Eric (2012) 34th Annual Conference of the IEEE EMBS In Proceedings of the 34th Annual Conference of the IEEE EMBS p.2839-2844
Abstract
This paper presents a novel on-line approach of merging multiple different predictors of plasma glucose into a single optimized prediction. Various different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on 12 data sets of type I diabetes data, using three parallel predictors. The performance of the combined prediction is better, or in par, with the best predictor for each evaluated data set. The results suggest that the outlined method could be a suitable way to improve prediction performance when using multiple predictors, or as a means to reduce the risk associated with definite a priori model selection.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Proceedings of the 34th Annual Conference of the IEEE EMBS
editor
Khoo, Michael
pages
6 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
34th Annual Conference of the IEEE EMBS
external identifiers
  • WOS:000313296503017
project
DIAdvisor
language
English
LU publication?
yes
id
12e4a623-c2c5-4e2d-8b8d-9e0f07da2c6b (old id 3242079)
date added to LUP
2012-12-14 12:39:14
date last changed
2016-04-16 10:31:54
@misc{12e4a623-c2c5-4e2d-8b8d-9e0f07da2c6b,
  abstract     = {This paper presents a novel on-line approach of merging multiple different predictors of plasma glucose into a single optimized prediction. Various different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on 12 data sets of type I diabetes data, using three parallel predictors. The performance of the combined prediction is better, or in par, with the best predictor for each evaluated data set. The results suggest that the outlined method could be a suitable way to improve prediction performance when using multiple predictors, or as a means to reduce the risk associated with definite a priori model selection.},
  author       = {Ståhl, Fredrik and Johansson, Rolf and Renard, Eric},
  editor       = {Khoo, Michael},
  language     = {eng},
  pages        = {2839--2844},
  publisher    = {ARRAY(0x8fc1830)},
  series       = {Proceedings of the 34th Annual Conference of the IEEE EMBS},
  title        = {Bayesian Combination of Multiple Plasma Glucose Predictors},
  year         = {2012},
}