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Selection bias in the reported performances of AD classification pipelines.

Abstract：

:The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half) of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation.

journal_name

Neuroimage Clin

journal_title

NeuroImage. Clinical

authors

Mendelson AF,Zuluaga MA,Lorenzi M,Hutton BF,Ourselin S,Alzheimer's Disease Neuroimaging Initiative.

doi

10.1016/j.nicl.2016.12.018

subject

Has Abstract

pub_date

2016-12-24 00:00:00

pages

400-416

issn

2213-1582

pii

S2213-1582(16)30256-X

journal_volume

14

pub_type

杂志文章

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