Performance reproducibility index for classification.

Abstract

A common practice in biomarker discovery is to decide whether a large laboratory experiment should be carried out based on the results of a preliminary study on a small set of specimens. Consideration of the efficacy of this approach motivates the introduction of a probabilistic measure, for whether a classifier showing promising results in a small-sample preliminary study will perform similarly on a large independent sample. Given the error estimate from the preliminary study, if the probability of reproducible error is low, then there is really no purpose in substantially allocating more resources to a large follow-on study. Indeed, if the probability of the preliminary study providing likely reproducible results is small, then why even perform the preliminary study? This article introduces a reproducibility index for classification, measuring the probability that a sufficiently small error estimate on a small sample will motivate a large follow-on study. We provide a simulation study based on synthetic distribution models that possess known intrinsic classification difficulties and emulate real-world scenarios. We also set up similar simulations on four real datasets to show the consistency of results. The reproducibility indices for different distributional models, real datasets and classification schemes are empirically calculated. The effects of reporting and multiple-rule biases on the reproducibility index are also analyzed. We have implemented in C code the synthetic data distribution model, classification rules, feature selection routine and error estimation methods. The source code is available at http://gsp.tamu.edu/Publications/supplementary/yousefi12a/.