  Two indicators are classically used to evaluate the quality of rule-based
classification systems: predictive accuracy, i.e. the system's ability to
successfully reproduce learning data and coverage, i.e. the proportion of
possible cases for which the logical rules constituting the system apply. In
this work, we claim that these two indicators may be insufficient, and
additional measures of quality may need to be developed. We theoretically show
that classification systems presenting "good" predictive accuracy and coverage
can, nonetheless, be trivially improved and illustrate this proposition with
examples.

Artificial Intelligence;Machine Learning;Logic in Computer Science