Stabilizing linear prediction models using autoencoder

Gopakumar, S, Tran, Truyen, Phung, Quoc-Dinh and Venkatesh, Svetha 2016, Stabilizing linear prediction models using autoencoder, in ADMA 2016 : Proceedings of the 12th International Conference for Advanced Data Mining and Applications, Springer International Publishing, Cham, Switzerland, pp. 651-663, doi: 10.1007/978-3-319-49586-6_46.

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Title Stabilizing linear prediction models using autoencoder
Author(s) Gopakumar, S
Tran, TruyenORCID iD for Tran, Truyen
Phung, Quoc-DinhORCID iD for Phung, Quoc-Dinh
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Conference name Advanced Data Mining and Applications. International Conference (12th : 2016 : Gold Coast, Queensland)
Conference location Gold Coast, Queensland
Conference dates 2016/12/12 - 2016/12/15
Title of proceedings ADMA 2016 : Proceedings of the 12th International Conference for Advanced Data Mining and Applications
Editor(s) Li, J
Li, X
Wang, S
Li, J
Sheng, QZ
Publication date 2016
Series Lecture notes in artificial intelligence
Start page 651
End page 663
Total pages 13
Publisher Springer International Publishing
Place of publication Cham, Switzerland
Summary To date, the instability of prognostic predictors in a sparse high dimensional model, which hinders their clinical adoption, has received little attention. Stable prediction is often overlooked in favour of performance. Yet, stability prevails as key when adopting models in critical areas as healthcare. Our study proposes a stabilization scheme by detecting higher order feature correlations. Using a linear model as basis for prediction, we achieve feature stability by regularizing latent correlation in features. Latent higher order correlation among features is modelled using an autoencoder network. Stability is enhanced by combining a recent technique that uses a feature graph, and augmenting external unlabelled data for training the autoencoder network. Our experiments are conducted on a heart failure cohort from an Australian hospital. Stability was measured using Consistency index for feature subsets and signal-to-noise ratio for model parameters. Our methods demonstrated significant improvement in feature stability and model estimation stability when compared to baselines.
ISBN 9783319495859
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-319-49586-6_46
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2016, Springer International Publishing
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