  Rule-based classifier, that extract a subset of induced rules to efficiently
learn/mine while preserving the discernibility information, plays a crucial
role in human-explainable artificial intelligence. However, in this era of big
data, rule induction on the whole datasets is computationally intensive. So
far, to the best of our knowledge, no known method focusing on accelerating
rule induction has been reported. This is first study to consider the
acceleration technique to reduce the scale of computation in rule induction. We
propose an accelerator for rule induction based on fuzzy rough theory; the
accelerator can avoid redundant computation and accelerate the building of a
rule classifier. First, a rule induction method based on consistence degree,
called Consistence-based Value Reduction (CVR), is proposed and used as basis
to accelerate. Second, we introduce a compacted search space termed Key Set,
which only contains the key instances required to update the induced rule, to
conduct value reduction. The monotonicity of Key Set ensures the feasibility of
our accelerator. Third, a rule-induction accelerator is designed based on Key
Set, and it is theoretically guaranteed to display the same results as the
unaccelerated version. Specifically, the rank preservation property of Key Set
ensures consistency between the rule induction achieved by the accelerator and
the unaccelerated method. Finally, extensive experiments demonstrate that the
proposed accelerator can perform remarkably faster than the unaccelerated
rule-based classifier methods, especially on datasets with numerous instances.
