ProtoGate: Prototype-based Neural Networks with Local Feature Selection for Tabular Biomedical Data

by   Xiangjian Jiang, et al.
University of Cambridge

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.


Feature Selection Based on Sparse Neural Network Layer with Normalizing Constraints

Feature selection is important step in machine learning since it has sho...

Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data

Tabular biomedical data is often high-dimensional but with a very small ...

An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data

Feature selection has been studied widely in the literature. However, th...

Interpretable Multiple-Kernel Prototype Learning for Discriminative Representation and Feature Selection

Prototype-based methods are of the particular interest for domain specia...

Dynamic Instance-Wise Classification in Correlated Feature Spaces

In a typical supervised machine learning setting, the predictions on all...

Feature-Wise Bias Amplification

We study the phenomenon of bias amplification in classifiers, wherein a ...

Interpretable Deep Learning Methods for Multiview Learning

Technological advances have enabled the generation of unique and complem...

Please sign up or login with your details

Forgot password? Click here to reset