ProtoGate: Prototype-based Neural Networks with Local Feature Selection
  for Tabular Biomedical Data
Xiangjian Jiang Andrei Margeloiu Nikola Simidjievski Mateja Jamnik
Tabular biomedical data poses challenges in machine learning because it is
often high-dimensional and typically low-sample-size. Previous research has
attempted to address these challenges via feature selection approaches, which
can lead to unstable performance on real-world data. This suggests that current
methods lack appropriate inductive biases that capture patterns common to
different samples. In this paper, we propose ProtoGate, a prototype-based
neural model that introduces an inductive bias by attending to both homogeneity
and heterogeneity across samples. ProtoGate selects features in a
global-to-local manner and leverages them to produce explainable predictions
via an interpretable prototype-based model. We conduct comprehensive
experiments to evaluate the performance of ProtoGate on synthetic and
real-world datasets. Our results show that exploiting the homogeneous and
heterogeneous patterns in the data can improve prediction accuracy while
prototypes imbue interpretability.