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On selecting interacting features from high-dimensional data

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. Green open access

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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 > Provost and Vice Provost Offices
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: http://discovery.ucl.ac.uk/id/eprint/1373834
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