Hall, P;
Xue, J-H;
(2014)
On selecting interacting features from high-dimensional data.
Computational Statistics & Data Analysis
, 71
pp. 694-708.
10.1016/j.csda.2012.10.010.
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Abstract
For high-dimensional data, most feature-selection methods, such as SIS and the lasso, involve ranking and selecting features individually. These methods do not require many computational resources, but they ignore feature interactions. A simple recursive approach, which, without requiring many more computational resources, also allows identification of interactions, is investigated. This approach can lead to substantial improvements in the performance of classifiers, and can provide insight into the way in which features work together in a given population. It also enjoys attractive statistical properties.
| Type: | Article |
|---|---|
| Title: | On selecting interacting features from high-dimensional data |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.csda.2012.10.010 |
| Publisher version: | https://doi.org/10.1016/j.csda.2012.10.010 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Classification, Correlation, Generalised correlation, Feature ranking |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1373834 |
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