  Interpretability is the next frontier in machine learning research. In the
search for white box models - as opposed to black box models, like random
forests or neural networks - rule induction algorithms are a logical and
promising option, since the rules can easily be understood by humans. Fuzzy and
rough set theory have been successfully applied to this archetype, almost
always separately. As both approaches to rule induction involve granular
computing based on the concept of equivalence classes, it is natural to combine
them. The QuickRules\cite{JensenCornelis2009} algorithm was a first attempt at
using fuzzy rough set theory for rule induction. It is based on QuickReduct, a
greedy algorithm for building decision reducts. QuickRules already showed an
improvement over other rule induction methods. However, to evaluate the full
potential of a fuzzy rough rule induction algorithm, one needs to start from
the foundations. In this paper, we introduce a novel rule induction algorithm
called Fuzzy Rough Rule Induction (FRRI). We provide background and explain the
workings of our algorithm. Furthermore, we perform a computational experiment
to evaluate the performance of our algorithm and compare it to other
state-of-the-art rule induction approaches. We find that our algorithm is more
accurate while creating small rulesets consisting of relatively short rules. We
end the paper by outlining some directions for future work.
