Abstract
The search for higher-order feature interactions that
are statistically significantly associated with a class
variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational ef-
ficiency and proper correction for multiple testing.
While recent progress has been made regarding this
challenge for binary features, we here present the
first solution for continuous features. We propose
an algorithm which overcomes the combinatorial
explosion of the search space of higher-order interactions by deriving a lower bound on the p-value
for each interaction, which enables us to massively
prune interactions that can never reach significance
and to thereby gain more statistical power. In our
experiments, our approach efficiently detects all
significant interactions in a variety of synthetic and
real-world datasets