Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 26, Issue 4, Pages 880-886Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2014.2320415
Keywords
Feature selection (FS); Neyman-Pearson
Categories
Funding
- National Science Foundation CAREER [0845827]
- National Science Foundation [1120622, ECCS-0926159, ECCS-1310496]
- Department of Energy [SC004335]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [0845827] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1120622] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1418744] Funding Source: National Science Foundation
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Selection of most informative features that leads to a small loss on future data are arguably one of the most important steps in classification, data analysis and model selection. Several feature selection (FS) algorithms are available; however, due to noise present in any data set, FS algorithms are typically accompanied by an appropriate cross-validation scheme. In this brief, we propose a statistical hypothesis test derived from the Neyman-Pearson lemma for determining if a feature is statistically relevant. The proposed approach can be applied as a wrapper to any FS algorithm, regardless of the FS criteria used by that algorithm, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point. We provide freely available software implementations of the proposed methodology.
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