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With the growing adoption of deep learning models in different real-worlddomains, including computational biology, it is often necessary to understandwhich data features are essential for the model's decision. Despite extensiverecent efforts to define different feature importance metrics for deep learningmodels, we identified that inherent stochasticity in the design and training ofdeep learning models makes commonly used feature importance scores unstable.This results in varied explanations or selections of different features acrossdifferent runs of the model. We demonstrate how the signal strength of featuresand correlation among features directly contribute to this instability. Toaddress this instability, we explore the ensembling of feature importancescores of models across different epochs and find that this simple approach cansubstantially address this issue. For example, we consider knockoff inferenceas they allow feature selection with statistical guarantees. We discoverconsiderable variability in selected features in different epochs of deeplearning training, and the best selection of features doesn't necessarily occurat the lowest validation loss, the conventional approach to determine the bestmodel. As such, we present a framework to combine the feature importance oftrained models across different hyperparameter settings and epochs, and insteadof selecting features from one best model, we perform an ensemble of featureimportance scores from numerous good models. Across the range of experiments insimulated and various real-world datasets, we demonstrate that the proposedframework consistently improves the power of feature selection.

Ensembling improves stability and power of feature selection for deep learning models