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Adjusted binary classification (ABC) model in forensic science: an example on sex classification from handprint dimensions (CROSBI ID 290009)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Jerković, Ivan ; Kolić, Andrea ; Kružić, Ivana ; Anđelinović, Šimun ; Bašić, Željana Adjusted binary classification (ABC) model in forensic science: an example on sex classification from handprint dimensions // Forensic science international, 320 (2021), 110709, 8. doi: 10.1016/j.forsciint.2021.110709

Podaci o odgovornosti

Jerković, Ivan ; Kolić, Andrea ; Kružić, Ivana ; Anđelinović, Šimun ; Bašić, Željana

engleski

Adjusted binary classification (ABC) model in forensic science: an example on sex classification from handprint dimensions

Binary classification techniques are commonly used in forensic examination to test if a specimen belongs to a particular group and base the expert opinion on the questioned evidence. However, most of the currently used methods do not achieve sufficient accuracy due to the ignoring of the specimens classified in the overlapping area. To address the issue, we proposed a novel Adjusted binary classification (ABC) algorithm that automatically adjusts posterior probabilities to reach classification accuracy and positive/negative predicted values (PPV, NPV) of 95%. In the presented example, we used three handprint measurements from 160 participants (80 males and 80 females) to develop models that would classify sex from their dimensions. The sample was split into the training/cross-validated (70%) and testing sample (30%). We developed four classification models using linear discriminant analysis (LDA) by employing traditional single cut-off values and ABC approach that for each group provides a specific posterior probability cut-off threshold. In the cross- validated sample, the accuracy of traditional models was 78.7-92.5%, while PPVs/NPVs ranged between 78.2 and 93%. ABC models provided 95% accuracy, PPV, and NPV, and could classify 35.5-88.1% of specimens. In the testing sample, ABC models achieved accuracy of 97.3-100%, PPV/NPV 95.4-100%, and could be applied to 29.1-87.5% of specimens. The study demonstrated that the ABC approach could adjust classification models to reach predefined values of accuracy, PPV, and NPV. Therefore, it could be an efficient tool for conducting a binary classification in forensic settings and minimizing the possibilities of incorrect classifications.

binary classification ; discriminant analysis ; posterior probability ; forensic science ; handprints

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Podaci o izdanju

320

2021.

110709

8

objavljeno

0379-0738

1872-6283

10.1016/j.forsciint.2021.110709

Povezanost rada

Kognitivna znanost (prirodne, tehničke, biomedicina i zdravstvo, društvene i humanističke znanosti), Sigurnosne i obrambene znanosti, Temeljne medicinske znanosti

Poveznice
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