Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
This addresses the need for interpretable machine learning by offering a faster and accurate method for feature importance estimation, though it is incremental as it builds on existing mixture-of-experts and attention mechanisms.
The paper tackles the problem of estimating feature importance in neural networks by introducing Granger-causal Attentive Mixtures of Experts (AMEs), which learn to produce accurate predictions and feature importance estimates in a single model. The results show that AMEs provide feature importance estimates comparable to state-of-the-art methods, are significantly faster, and align with domain expert knowledge.
Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-the-art methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.