Validation of ground truth fire debris classification by supervised machine learning

Trullols, 2004, Validation of qualitative analytical methods, TrAC, Trends Anal. Chem., 23, 137, 10.1016/S0165-9936(04)00201-8 López, 2015, A tutorial on the validation of qualitative methods: From the univariate to the multivariate approach, Anal. Chim. Acta, 891, 62, 10.1016/j.aca.2015.06.032 Trullols, 2005, Validation of qualitative methods of analysis that use control samples, TrAC, Trends Anal. Chem., 24, 516, 10.1016/j.trac.2005.04.001 Wehling, 2011, Probability of detection (pod) as a statistical model for the validation of qualitative methods, J. AOAC Int., 94, 335, 10.1093/jaoac/94.1.335 International, 2014, Astm e1618–14, standard test method for ignitable liquid residues in extracts from fire debris samples by gas-chromatography- mass spectrometry ASTM International West Conchohocken, PA Newman, 2004, Astm approach to fire debris analysis, Analy. Interpret. Fire Scene Evid., 165, 10.1201/9780203492727.ch6 Jamieson, A. Accreditation: Laboratory. Wiley Encyclopedia of Forensic Science 2009, 1-6. Waddell, 2013, Progress toward the determination of correct classification rates in fire debris analysis, J. Forensic Sci., 58, 887, 10.1111/1556-4029.12159 Waddell, 2014, Progress toward the determination of correct classification rates in fire debris analysis ii: Utilizing soft independent modeling of class analogy (simca), J. Forensic Sci., 59, 927, 10.1111/1556-4029.12417 Lopatka, 2015, Class-conditional feature modeling for ignitable liquid classification with substantial substrate contribution in fire debris analysis, Forensic Sci. Int., 252, 177, 10.1016/j.forsciint.2015.04.035 Sigman, 2016, Assessing evidentiary value in fire debris analysis by chemometric and likelihood ratio approaches, Forensic Sci. Int., 264, 113, 10.1016/j.forsciint.2016.03.051 Allen, 2018, Model distribution effects on likelihood ratios in fire debris analysis, Separations, 5, 44, 10.3390/separations5030044 Coulson, 2018, Model-effects on likelihood ratios for fire debris analysis, Forensic Chem., 7, 38, 10.1016/j.forc.2017.12.008 Allen, 2019, Application of likelihood ratios and optimal decision thresholds in fire debris analysis based on a partial least squares discriminant analysis (pls-da) model, Forensic Chem., 16, 100188, 10.1016/j.forc.2019.100188 Thurn, 2021, Classification of ground-truth fire debris samples using artificial neural networks. Forensic, Chemistry, 23, 100313 Substrates database. http://ilrc.ucf.edu/substrate/. Ignitable liquids reference collection and database (ilrc). National Center for Forensic Science: online, 2017. Sigman, 2008, Ignitable liquid classification and identification using the summed-ion mass spectrum, Instrum Sci. Technol., 36, 375, 10.1080/10739140802151440 Jayatilaka, 1994, Identification of petroleum distillates from fire debris using multidimensional gas chromatography, Chromatographia, 39, 200, 10.1007/BF02274501 Fire debris database. http://ilrc.ucf.edu/firedebris/. Malinowski, 2009, Determination of rank by median absolute deviation (drmad): A simple method for determining the number of principal factors responsible for a data matrix, Journal of Chemometrics: A Journal of the Chemometrics Society, 23, 1, 10.1002/cem.1182 Kuhn, M. The caret package. R Foundation for Statistical Computing, Vienna, Austria. URL https://cran. r-project. org/package= caret 2012. Ripley, B.; Venables, B.; Bates, D.M.; Hornik, K.; Gebhardt, A.; Firth, D.; Ripley, M.B. Package ‘mass’. Cran R 2013, 538. Ripley, W.N.V.a.B.D. Modern applied statistics with s. Fourth ed.; Springer: New York, 2002. Sing, 2007, The rocr package, R Vignette Team“, R.C. R: A language and environment for statistical computing., R Foundation for Statistical Computing,: Vienna, Austria, 2013. Grzegorz Zadora, 2014 Aitken, 1998, A verbal scale for the interpretation of evidence, Sci. Justice, 38, 279, 10.1016/S1355-0306(98)72128-8 Williams, 2012, Combined target factor analysis and bayesian soft-classification of interference-contaminated samples: Forensic fire debris analysis, Forensic Sci. Int., 222, 373, 10.1016/j.forsciint.2012.07.021 J.n. Lewis, The application of chemometrics to the detection and classification of ignitable liquids in fire debris using the total ion spectrum. University of Central Florida, University of Central Florida, 2011. Hanley, 1982, The meaning and use of the area under a receiver operating characteristic (roc) curve, Radiology, 143, 29, 10.1148/radiology.143.1.7063747 Hearst, 1998, Support vector machines, IEEE Intellig. Syst. Appl., 13, 18, 10.1109/5254.708428 F. Provost, T. Fawcett, In Robust classification systems for imprecise environments, 1998 AAAI/IAAI, pp. 706-713. Provost, 2001, Robust classification for imprecise environments, Mach. Learn., 42, 203, 10.1023/A:1007601015854 Fawcett, 2006, Roc graphs with instance-varying costs, Pattern Recogn. Lett., 27, 882, 10.1016/j.patrec.2005.10.012 Choi, 1998, Slopes of a receiver operating characteristic curve and likelihood ratios for a diagnostic test, Am. J. Epidemiol., 148, 1127, 10.1093/oxfordjournals.aje.a009592 COLLINSON, 1998, Of bombers, radiologists, and cardiologists: Time to roc, Heart, 80, 215, 10.1136/hrt.80.3.215