Comparing the topological properties of real and artificially generated scientific manuscripts
Table 2: Accuracy rate (%) obtained when distinguishing real (PRL, arXiv/cs or JI) from artificial papers (SCIGen). Unlike the univariate analysis, all 13 topological measurements were employed.
['[BOLD] Dataset', '[BOLD] KNN', '[BOLD] NBY', '[BOLD] C45']
When one compares the results obtained here with the ones achieved with the univariate analyses, it is clear that the multivariate analysis improved the discriminative ability of the classifiers. The accuracy rate in the PRL dataset improved 10% (from 79% to 89%). In the arXiv/cs dataset, the accuracy went from 88% to 95%. The lowest increase in accuracy occurred for the JI dataset, which already had provided an excellent discriminability with the univariate approach. These results suggest that the interaction of attributes is able to improve the identification of fake papers generated by the SCIGen algorith, especially if the separation between real and fake papers is not so clear when a single measurement is employed to generate the classifiers.