A Step Towards InterpretableAuthorship Verification
Table 4: Adjusted hyperparameter ranges for the AV method Unmasking. Note that the most important modifications affect the hyperparameters U_{1} and U_{4}.
['[BOLD] Hyperparameter', '[BOLD] Our grid search range', '[BOLD] Original setting']
Regarding ProfAV, IM, Unmasking and GenUnmasking, we used our own implementations, where for the first two we employed the same hyperparameter ranges described in the original papers. In the original definition of this method, Koppel and Schler [KoppelAVOneClassClassification:2004, KoppelUnmasking:2007] considered entire books to train and evaluate Unmasking, which differ in lengths from the documents used in our experiments. Therefore, instead of using the original fixed hyperparameter settings (which would make Unmasking inapplicable in our evaluation setting), we decided to consider individual hyperparameter ranges with values that are more appropriate for shorter documents as available in our corpora. Hyperparameters of ProfAV, Unmasking and IM tuned on the respective training corpora with the following notation: L_{u} = Profile size of the unknown document; L_{k} = Profile size of the known document; n = The n-order of character n-grams; d = Dissimilarity function (cf. [StamatatosProfileCNG:2014, Section 3.1]); M = Number of most similar documents that serve as potential impostors; N = Number of actual impostors from among the potential impostors; k = Number of iterations.