Classification of ground-truth fire debris samples using artificial neural networks

Abstract

Neural networks are a class of biologically inspired machine learning models that are used for classification and regression problems. This work assesses the classification performance of neural networks on ground-truth fire debris samples using ions that are representative of several compound classes that are typically present in ignitable liquids. An optimal neural network model was selected from a subset of candidate models that were trained on in-silico mixed fire debris samples from the National Center for Forensic Science Substrate and Ignitable Liquid Reference Collection databases. An optimal decision threshold was determined using a defined ratio of misclassification costs. A cost ratio corresponding to a false positive classification having a cost that is 10 times greater than a false negative classification resulted in a decision threshold of log likelihood ratio of 0.966. This decision threshold resulted in a false positive rate of 0.07 and a true positive rate of 0.59 for the ground-truth validation data. This study demonstrates the selection of an optimal decision threshold using ROC analysis and exhibits the potential of neural network models for the evaluation of fire debris evidence.