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A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data
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 be...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5556617/ https://www.ncbi.nlm.nih.gov/pubmed/28835769 http://dx.doi.org/10.1155/2017/7907163 |
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author | Bommert, Andrea Rahnenführer, Jörg Lang, Michel |
author_facet | Bommert, Andrea Rahnenführer, Jörg Lang, Michel |
author_sort | Bommert, Andrea |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5556617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-55566172017-08-23 A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data Bommert, Andrea Rahnenführer, Jörg Lang, Michel Comput Math Methods Med Research Article 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. Hindawi 2017 2017-08-01 /pmc/articles/PMC5556617/ /pubmed/28835769 http://dx.doi.org/10.1155/2017/7907163 Text en Copyright © 2017 Andrea Bommert et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Bommert, Andrea Rahnenführer, Jörg Lang, Michel A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data |
title | A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data |
title_full | A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data |
title_fullStr | A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data |
title_full_unstemmed | A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data |
title_short | A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High-Dimensional Data |
title_sort | multicriteria approach to find predictive and sparse models with stable feature selection for high-dimensional data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5556617/ https://www.ncbi.nlm.nih.gov/pubmed/28835769 http://dx.doi.org/10.1155/2017/7907163 |
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