Composite Machine Learning Algorithm for Material Sourcing,. Issue 5 (28th April 2020)

• Record Type: Journal Article
• Authors: Casale, Amanda; Dettman, Josh
• Is Part Of: Journal of forensic sciences. Volume 65:Issue 5(2020)
• Page Start: 1458
• Page End: 1464
• Publication Date: 2020-04-28
• Subjects: forensic science -- source attribution -- k‐nearest neighbor -- multinomial logistic regression -- random forest; Medical jurisprudence -- Periodicals; Forensic sciences -- Periodicals; Forensic Medicine -- Periodicals; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; 363.2505
• DOI: 10.1111/1556-4029.14436
• Languages: English
• ISSNs: 0022-1198

Abstract

Abstract: This study developed a composite machine learning algorithm for attribution of materials of forensic interest (like ammonium nitrate) to original sources. k ‐nearest neighbor and random forest models were used for source elimination and classification, respectively, in a two‐step, composite algorithm based on particle color, size/shape, and trace element concentration features. Novel approaches for simulation to supplement within‐source reference features based on empirically measured multi‐lot analyses, an improved hold‐one‐lot‐out method for cross‐validation, an assessment of the likelihood of the presence of a reference sample, fusion of the source probabilities from the respective classification models, and the calculation of metrics for assessing ensemble sourcing performance are described. Excellent sourcing predictions were obtained; the sourcing algorithm identified the correct source as the top choice 89% of the time, and the correct source was identified to be an average of 2.7 times more likely than the most likely incorrect source.