  RRULES is presented as an improvement and optimization over RULES, a simple
inductive learning algorithm for extracting IF-THEN rules from a set of
training examples. RRULES optimizes the algorithm by implementing a more
effective mechanism to detect irrelevant rules, at the same time that checks
the stopping conditions more often. This results in a more compact rule set
containing more general rules which prevent overfitting the training set and
obtain a higher test accuracy. Moreover, the results show that RRULES
outperforms the original algorithm by reducing the coverage rate up to a factor
of 7 while running twice or three times faster consistently over several
datasets.
