Finding Interpretable Class-Specific Patterns through Efficient Neural
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Nils Philipp Walter Jonas Fischer Jilles Vreeken
Discovering patterns in data that best describe the differences between
classes allows to hypothesize and reason about class-specific mechanisms. In
molecular biology, for example, this bears promise of advancing the
understanding of cellular processes differing between tissues or diseases,
which could lead to novel treatments. To be useful in practice, methods that
tackle the problem of finding such differential patterns have to be readily
interpretable by domain experts, and scalable to the extremely high-dimensional
data.
  In this work, we propose a novel, inherently interpretable binary neural
network architecture DIFFNAPS that extracts differential patterns from data.
DiffNaps is scalable to hundreds of thousands of features and robust to noise,
thus overcoming the limitations of current state-of-the-art methods in
large-scale applications such as in biology. We show on synthetic and real
world data, including three biological applications, that, unlike its
competitors, DiffNaps consistently yields accurate, succinct, and interpretable
class descriptions