Predicting Seminal Quality with the Dominance-Based Rough Sets Approach
  The paper relies on the clinical data of a previously published study. We
identify two very questionable assumptions of said work, namely confusing
evidence of absence and absence of evidence, and neglecting the ordinal nature
of attributes' domains. We then show that using an adequate ordinal methodology
such as the dominance-based rough sets approach (DRSA) can significantly
improve the predictive accuracy of the expert system, resulting in almost
complete accuracy for a dataset of 100 instances. Beyond the performance of
DRSA in solving the diagnosis problem at hand, these results suggest the
inadequacy and triviality of the underlying dataset. We provide links to open
data from the UCI machine learning repository to allow for an easy
verification/refutation of the claims made in this paper.
