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Microscopic Saw Mark Analysis: An Empirical Approach
Author(s) -
Love Jennifer C.,
Derrick Sharon M.,
Wiersema Jason M.,
Peters Charles
Publication year - 2015
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12650
Subject(s) - word error rate , random forest , statistical analysis , statistics , computer science , decision tree , outcome (game theory) , artificial intelligence , mathematics , mathematical economics
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 presented 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. 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 K eyence 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%.