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 c...
| Publicado en: | Forensic Science International Vol. 320 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Elsevier B.V.
Mar2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148983983&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148983983 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03790738 3L0 jtl: Forensic Science International issn: 03790738 maglogo: N pubinfo: dt: Mar2021 vid: 320 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 148983983 148983983 NLM33549991 148983983 10.1016/j.forsciint.2021.110709 NLM33549991 148983983 ppct: 1 formats: tig: atl: Adjusted binary classification (ABC) model in forensic science: An example on sex classification from handprint dimensions. aug: au: Jerković, Ivan Kolić, Andrea Kružić, Ivana Anđelinović, Šimun Bašić, Željana affil: University Department of Forensic Sciences, University of Split, Ruđera Boškovića 33, 21000 Split, Croatia sug: subj: Diagnosis, Laboratory Methods Hand Anatomy and Histology Forensic Sciences Image Processing, Computer Assisted Dyes Male Discriminant Analysis Female Predictive Value of Tests Human Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|