Title: Dropout Feature Ranking for Deep Learning Models,
Abstract: Deep neural networks (DNNs) achieve state-of-the-art results in a variety of
domains. Unfortunately, DNNs are notorious for their non-interpretability, and
thus limit their applicability in hypothesis-driven domains such as biology and
healthcare. Moreover, in the resource-constraint setting, it is critical to
design tests relying on fewer more informative features leading to high
accuracy performance within reasonable budget. We aim to close this gap by
proposing a new general feature ranking method for deep learning. We show that
our simple yet effective method performs on par or compares favorably to eight
strawman, classical and deep-learning feature ranking methods in two
simulations and five very different datasets on tasks ranging from
classification to regression, in both static and time series scenarios. We also
illustrate the use of our method on a drug response dataset and show that it
identifies genes relevant to the drug-response.