Study design in high-dimensional classification analysis

Abstract

SUMMARYAdvances in high throughput technology have accelerated the use of hundreds to millions of biomarkers to construct classifiers that partition patients into different clinical conditions. Prior to classifier development in actual studies, a critical need is to determine the sample size required to reach a specified classification precision. We develop a systematic approach for sample size determination in high-dimensional (large p small n) classification analysis. Our method utilizes the probability of correct classification (PCC) as the optimization objective function and incorporates the higher criticism thresholding procedure for classifier development. Further, we derive the theoretical bound of maximal PCC gain from feature augmentation (e.g. when molecular and clinical predictors are combined in classifier development). Our methods are motivated and illustrated by a study using proteomics markers to classify post-kidney transplantation patients into stable and rejecting classes.

Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review

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Cost-sensitive Feature Selection for Support Vector Machines

“…[1,2], full of challenges from a Mathematical Optimization perspective, e.g. [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17].…”

Optimal design for classification of functional data

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