Construction and evaluation of classifiers for forensic document analysis
Applications
2015-03-14 v2 Methodology
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.
Keywords
Cite
@article{arxiv.1004.0678,
title = {Construction and evaluation of classifiers for forensic document analysis},
author = {Christopher P. Saunders and Linda J. Davis and Andrea C. Lamas and John J. Miller and Donald T. Gantz},
journal= {arXiv preprint arXiv:1004.0678},
year = {2015}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AOAS379 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)