The Neyman-Pearson (NP) classification paradigm addresses an important binary classification problem where users want to minimize type II error while controlling type I error under some specified level α, usually a small number. This problem is often faced in many genomic applications involving binary classification tasks. The terminology Neyman-Pearson classification paradigm arises from its connection to the Neyman-Pearson paradigm in hypothesis testing. The NP paradigm is applicable when one type of error (e.g., type I error) is far more important than the other type (e.g., type II error), and users have a specific target bound for the former. In this chapter, we review the NP classification literature, with a focus on the genomic applications as well as our contribution to the NP classification theory and algorithms. We also provide simulation examples and a genomic case study to demonstrate how to use the NP classification algorithm in practice.
CITATION STYLE
Li, J. J., & Tong, X. (2016). Genomic applications of the neyman-pearson classification paradigm. In Big Data Analytics in Genomics (pp. 145–167). Springer International Publishing. https://doi.org/10.1007/978-3-319-41279-5_4
Mendeley helps you to discover research relevant for your work.