Neyman-pearson classiffication under high-dimensional settings
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15-418.pdf
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795.16 KB
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Author(s)
Zhao, Anqi
Feng, Yang
Wang, Lie
Tong, Xin
Date Issued
January 2016
Journal
Journal of Machine Learning Research
Publisher
JMLR, Inc.
Citation
Zhao, Anqi et al. "Neyman-Pearson Classification under High-Dimensional
Settings." Journal of Machine Learning Research, 17, 2016, pp. 7469-7507
Version
Final published version
Abstract
Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error and a constrained type I error under a user specified level. This article is the first attempt to construct classifiers with guaranteed theoretical performance under the NP paradigm in high-dimensional settings. Based on the fundamental Neyman-Pearson Lemma, we used a plug-in approach to construct NP-Type classifiers for Naive Bayes models. The proposed classifiers satisfy the NP oracle inequalities, which are natural NP paradigm counterparts of the oracle inequalities in classical binary classification. Besides their desirable theoretical properties, we also demonstrated their numerical advantages in prioritized error control via both simulation and real data studies.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://dl.acm.org/citation.cfm?id=3053494
