Bayesian Combination of Multiple Plasma Glucose Predictors
Ståhl, Fredrik LU ; Johansson, Rolf LU
and Renard, Eric
(2012)
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.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3242079
- author
- Ståhl, Fredrik
LU
; Johansson, Rolf
LU
and Renard, Eric
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 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
- conference location
- San Diego, CA, United States
- conference dates
- 2012-08-28
- external identifiers
-
- wos:000313296503017
- scopus:84883027374
- project
- DIAdvisor
- language
- English
- LU publication?
- yes
- id
- 12e4a623-c2c5-4e2d-8b8d-9e0f07da2c6b (old id 3242079)
- date added to LUP
- 2016-04-04 12:24:30
- date last changed
- 2024-01-13 04:49:34
@inproceedings{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}},
booktitle = {{Proceedings of the 34th Annual Conference of the IEEE EMBS}},
editor = {{Khoo, Michael}},
language = {{eng}},
pages = {{2839--2844}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Bayesian Combination of Multiple Plasma Glucose Predictors}},
url = {{https://lup.lub.lu.se/search/files/5998094/3242081.pdf}},
year = {{2012}},
}