Journal article
A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance
IEEE transaction on neural networks and learning systems, v 26(4), pp 880-886
01 Apr 2015
PMID: 25794384
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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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Details
- Title
- A Bootstrap Based Neyman-Pearson Test for Identifying Variable Importance
- Creators
- Gregory Ditzler - Drexel UniversityRobi Polikar - Rowan UniversityGail Rosen - Drexel University
- Publication Details
- IEEE transaction on neural networks and learning systems, v 26(4), pp 880-886
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- SC004335 / Department of Energy; United States Department of Energy (DOE) 0845827 / National Science Foundation CAREER; National Science Foundation (NSF) 0845827 / Direct For Biological Sciences; National Science Foundation (NSF); NSF - Directorate for Biological Sciences (BIO) 1120622; ECCS-0926159; ECCS-1310496 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000351835900018
- Scopus ID
- 2-s2.0-85028170986
- Other Identifier
- 991019169626104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Hardware & Architecture
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic