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Activity Number: 426 - SPEED: Biopharmaceutical and General Health Studies: Statistical Methods and Applications, Part 2
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 3:05 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #307844
Title: The Use of a New Classifier to Maximize the Classification Performance
Author(s): Hua Ma* and Joe Heyse
Companies: Merck and Merck
Keywords: ROC; distribution overlap; classification accuracy; Lanchenbruch's ?; machine learning; AUROC

Statistical methods have been constantly developed for maximizing classification performance. The overall classification performance for a diagnostic test can be evaluated based on different metrics, e.g. the area under the ROC curve (AUROC), Lachenbruch’s ?, the measure of distribution overlap, and the overall classification accuracy. New classifiers are proposed by synthesizing densities based upon Fisher’s method and direct computation of multivariate density function. Under the commonly used criteria for evaluating overall classification accuracy mentioned above, the classification performance of the proposed methods will be compared using statistical simulation and real data examples with the current popular established methods, e.g. regression, and machine learning methods such as random forest and support-vector machine (SVM). Different simulation scenarios will be considered and implemented.

Authors who are presenting talks have a * after their name.

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