On selecting interacting features from high-dimensional data

Peter Hall and Jing-Hao Xue

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 694-708

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.

Keywords: Classification; Correlation; Generalised correlation; Feature ranking (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:694-708

DOI: 10.1016/j.csda.2012.10.010

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