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Updated: Nov 2, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
A comprehensive examination of the cross-validity of pareto-optimal versus fixed-weight selection systems in the
Wilfried De Corte1, Filip Lievens2, Paul R Sackett3
1Department of Data-Analysis, Faculty of Psychology, Ghent University.
Abstract:
The article presents evidence for the cross-validity potential of fixed-weight (FW) versus Pareto-Optimal (PO) selection systems in biobjective selection situations where both the goals of diversity and quality are valued and the importance of the goals is undecided a priori. The article extends previous research by also studying the cross-validity potential of selection systems in the practically most important sample-to-sample cross-validity scenario. We address three research questions: (a) Do different PO systems show comparable levels of relative (i.e., proportional) achievement upon cross-validation? (b) Do PO systems achieve higher levels of relative achievement upon cross-validation than FW selection systems?, and (c) How does the achievement of PO and FW systems, in terms of adverse impact ratios and average performance of the selected applicants, evolve under cross-validation? As a key result, in case of sufficiently large applicant pools (typically 100 applicants or more), PO systems had on average a higher cross-validity potential than the corresponding FW systems. Yet, even for applicant pools as large as 500, FW systems may match the merits of PO systems and we present a straightforward procedure to decide which FW systems may offer a comparable cross-validation potential than the PO systems. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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