An Automated Text Categorization Framework based on Hyperparameter Optimization
Table 2: Authorship Attribution Data sets.
['Dataset', '[BOLD] macro-F1 Cummins\xa0', '[BOLD] macro-F1 Escalante ', '[BOLD] macro-F1  [ITALIC] μTC']
The first task analyzed is authorship attribution, The pre-processing stage of the μTC’s input is all-terms; others use the stemmed stage. The best performing classifiers are created by μTC, except for NFL where alternatives perform better. In the case of Business, Escalante et. Please notice that NFL and Bussiness are among the smaller dataset we tested, the low performance of μTC can be produced by the low number of exemplars, while alternative schemes take advantage of the few samples to compute better weights. In this benchmark, μTC produces the best result in all average cases. In a fine-grained comparison, only Meina surpasses μTC on the gender identification for English. This is also true for larger datasets like those depicted in Figures The figures show that even on k=2 the μTC achieves almost its optimal actual performance; even when the predicted performance is most of the times better for larger k values. On the other hand, the binary partition method is prone to overfit, especially on small datasets and small 1−β values (i.e., small test sets).