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Neyman-Pearson classification algorithms and NP receiver operating characteristics.

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  • 1Department of Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA 90089, USA.

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This study introduces a new algorithm and graphical tool to properly implement Neyman-Pearson (NP) classification, ensuring controlled Type I errors in applications like disease diagnosis and spam detection.

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Area of Science:

  • Machine Learning
  • Statistical Classification

Background:

  • Binary classification often requires strict control of Type I errors (misclassifying class 0 as class 1).
  • The Neyman-Pearson (NP) paradigm offers a principled approach to minimize Type II errors while bounding Type I errors, but its practical implementation in classification is lacking.
  • Current methods for limiting empirical Type I errors often fail to meet the desired control levels.

Purpose of the Study:

  • To develop a robust algorithm for implementing the NP classification paradigm across various scoring-based classifiers.
  • To introduce a novel graphical tool, NP Receiver Operating Characteristic (NP-ROC) bands, for selecting thresholds and comparing NP classifiers.
  • To provide a practical solution for accurate Type I error control in classification tasks.

Main Methods:

  • Development of the first "umbrella algorithm" to integrate the NP paradigm with scoring-type classifiers (e.g., logistic regression, SVM, random forests).
  • Introduction of NP-ROC bands as a visual aid for threshold selection and classifier comparison, analogous to standard ROC curves.
  • Validation through simulations and real-world data analysis, with implementation available in the R package 'nproc'.

Main Results:

  • The proposed umbrella algorithm effectively implements the NP paradigm, ensuring reliable Type I error control.
  • NP-ROC bands provide an intuitive and data-adaptive method for choosing the Type I error threshold (α) and evaluating NP classifiers.
  • Demonstrated superior performance and practical utility of the NP approach compared to common practices.

Conclusions:

  • The developed NP umbrella algorithm and NP-ROC bands offer a significant advancement for reliable Type I error control in binary classification.
  • These tools address the practical limitations of implementing the NP paradigm, enhancing its applicability in fields like medical diagnosis and spam filtering.
  • The R package 'nproc' makes these advanced NP classification methods accessible for broader use.