HDSI: High dimensional selection with interactions algorithm on feature selection and testing.
Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0246159 |
| _version_ | 1828919142036537344 |
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| author | Rahi Jain Wei Xu |
| author_facet | Rahi Jain Wei Xu |
| author_sort | Rahi Jain |
| collection | DOAJ |
| description | Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO. |
| first_indexed | 2024-12-13T21:22:09Z |
| format | Article |
| id | doaj.art-2db3e6c979b2487fa65a11586d40fa90 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T21:22:09Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2db3e6c979b2487fa65a11586d40fa902022-12-21T23:31:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01162e024615910.1371/journal.pone.0246159HDSI: High dimensional selection with interactions algorithm on feature selection and testing.Rahi JainWei XuFeature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO.https://doi.org/10.1371/journal.pone.0246159 |
| spellingShingle | Rahi Jain Wei Xu HDSI: High dimensional selection with interactions algorithm on feature selection and testing. PLoS ONE |
| title | HDSI: High dimensional selection with interactions algorithm on feature selection and testing. |
| title_full | HDSI: High dimensional selection with interactions algorithm on feature selection and testing. |
| title_fullStr | HDSI: High dimensional selection with interactions algorithm on feature selection and testing. |
| title_full_unstemmed | HDSI: High dimensional selection with interactions algorithm on feature selection and testing. |
| title_short | HDSI: High dimensional selection with interactions algorithm on feature selection and testing. |
| title_sort | hdsi high dimensional selection with interactions algorithm on feature selection and testing |
| url | https://doi.org/10.1371/journal.pone.0246159 |
| work_keys_str_mv | AT rahijain hdsihighdimensionalselectionwithinteractionsalgorithmonfeatureselectionandtesting AT weixu hdsihighdimensionalselectionwithinteractionsalgorithmonfeatureselectionandtesting |