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arXiv · 1004.0678

Construction and evaluation of classifiers for forensic document analysis

Christopher P. Saunders·Linda J. Davis·Andrea C. Lamas·John J. Miller·Donald T. Gantz
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Abstract

In this study we illustrate a statistical approach to questioned document examination. Specifically, we consider the construction of three classifiers that predict the writer of a sample document based on categorical data. To evaluate these classifiers, we use a data set with a large number of writers and a small number of writing samples per writer. Since the resulting classifiers were found to have near perfect accuracy using leave-one-out cross-validation, we propose a novel Bayesian-based cross-validation method for evaluating the classifiers.

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Christopher P. Saunders, Linda J. Davis, Andrea C. Lamas, John J. Miller, Donald T. Gantz. 2010-04-05. Construction and evaluation of classifiers for forensic document analysis. https://doi.org/10.1214/10-aoas379

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