NCJ Number
248808
Journal
Journal of Forensic Sciences Dated: 2014
Date Published
January 2015
Length
6 pages
Annotation
This study proposes the use of classification trees and random forest classifiers as an optimal, statistically sound approach to mitigate the potential for error of variability and outcome error in microscopic saw-mark analysis.
Abstract
Microscopic saw-mark analysis is a well-published and generally accepted qualitative analytical method; however, little research has focused on identifying and mitigating potential sources of error associated with the method. The statistical model was applied to 58 experimental saw marks created with four types of saws. The saw marks were made in fresh human femurs obtained through anatomical gift and were analyzed using a Keyence digital microscope. The statistical approach weighed the variables based on discriminatory value and produced decision trees with an associated outcome error rate of 8.62-17.82 percent. (Publisher abstract modified)
Date Published: January 1, 2015
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