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. Attached Files Name Description MIMEType Size Downloads Title Stabilizing linear prediction models using autoencoder Author(s) Gopakumar, S Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Quoc-Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 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 1611-3349 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 Persistent URL http://hdl.handle.net/10536/DRO/DU:30091398 Document type: Conference Paper Collection: School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 17 Feb 2017, 16:37:56 EST Sat, 18 Feb 2017, 04:01:14 EST Thu, 08 Jun 2017, 11:00:12 EST Thu, 08 Jun 2017, 11:01:43 EST Thu, 08 Jun 2017, 11:02:24 EST Wed, 14 Jun 2017, 06:05:40 EST Wed, 14 Jun 2017, 15:21:25 EST Fri, 02 Feb 2018, 14:11:07 EST Fri, 02 Feb 2018, 14:12:42 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 83 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Fri, 17 Feb 2017, 16:37:56 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.