A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data
Joint Authors
Bommert, Andrea
Lang, Michel
Rahnenführer, Jörg
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-01
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Finding a good predictive model for a high-dimensional data set can be challenging.
For genetic data, it is not only important to find a model with high predictive accuracy, but it is also important that this model uses only few features and that the selection of these features is stable.
This is because, in bioinformatics, the models are used not only for prediction but also for drawing biological conclusions which makes the interpretability and reliability of the model crucial.
We suggest using three target criteria when fitting a predictive model to a high-dimensional data set: the classification accuracy, the stability of the feature selection, and the number of chosen features.
As it is unclear which measure is best for evaluating the stability, we first compare a variety of stability measures.
We conclude that the Pearson correlation has the best theoretical and empirical properties.
Also, we find that for the stability assessment behaviour it is most important that a measure contains a correction for chance or large numbers of chosen features.
Then, we analyse Pareto fronts and conclude that it is possible to find models with a stable selection of few features without losing much predictive accuracy.
American Psychological Association (APA)
Bommert, Andrea& Rahnenführer, Jörg& Lang, Michel. 2017. A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data. Computational and Mathematical Methods in Medicine،Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1142328
Modern Language Association (MLA)
Bommert, Andrea…[et al.]. A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data. Computational and Mathematical Methods in Medicine No. 2017 (2017), pp.1-18.
https://search.emarefa.net/detail/BIM-1142328
American Medical Association (AMA)
Bommert, Andrea& Rahnenführer, Jörg& Lang, Michel. A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data. Computational and Mathematical Methods in Medicine. 2017. Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1142328
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142328