Ensemble learning for score likelihood ratios under the common source problem

dc.contributor.author Veneri, Federico
dc.contributor.author Ommen, Danica M.
dc.contributor.department Center for Statistics and Applications in Forensic Evidence
dc.contributor.department Statistics
dc.date.accessioned 2023-11-08T19:09:57Z
dc.date.available 2023-11-08T19:09:57Z
dc.date.issued 2023-08-04
dc.description.abstract Machine learning-based score likelihood ratios (SLRs) have emerged as alternatives to traditional likelihood ratios and Bayes factors to quantify the value of evidence when contrasting two opposing propositions. When developing a conventional statistical model is infeasible, machine learning can be used to construct a (dis)similarity score for complex data and estimate the ratio of the conditional distributions of the scores. Under the common source problem, the opposing propositions address if two items come from the same source. To develop their SLRs, practitioners create datasets using pairwise comparisons from a background population sample. These comparisons result in a complex dependence structure that violates the independence assumption made by many popular methods. We propose a resampling step to remedy this lack of independence and an ensemble approach to enhance the performance of SLR systems. First, we introduce a source-aware resampling plan to construct datasets where the independence assumption is met. Using these newly created sets, we train multiple base SLRs and aggregate their outputs into a final value of evidence. Our experimental results show that this ensemble SLR can outperform a traditional SLR approach in terms of the rate of misleading evidence and discriminatory power and present more consistent results.
dc.description.comments This article is published as F. Veneri and D. M. Ommen, Ensemble learning for score likelihood ratios under the common source problem, Stat. Anal. Data Min.: ASA Data Sci. J. 16 (2023), 528–546. https://doi.org/10.1002/sam.11637. © 2023 The Authors. Posted with permission of CSAFE.<br/><br/>This is an open access article under the terms of the <a href="https://creativecommons.org/licenses/by-nc/4.0/" target="_blank">Creative Commons Attribution-NonCommercial</a> License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/5w5p7Xoz
dc.language.iso en
dc.publisher Wiley Periodicals LLC
dc.source.uri https://doi.org/10.1002/sam.11637 *
dc.subject.disciplines DegreeDisciplines::Social and Behavioral Sciences::Legal Studies::Forensic Science and Technology
dc.subject.keywords common source problem
dc.subject.keywords ensemble learning
dc.subject.keywords forensic comparison
dc.subject.keywords handwriting analysis
dc.subject.keywords score likelihood ratios
dc.title Ensemble learning for score likelihood ratios under the common source problem
dc.type Article
dspace.entity.type Publication
relation.isOrgUnitOfPublication d8a3c72b-850f-40f6-87c4-8812547080c7
relation.isOrgUnitOfPublication 5a1eba07-b15d-466a-a333-65bd63a4001a
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