Estimating Conditional Mutual Information for Dynamic Feature Selection

• URL: http://arxiv.org/abs/2306.03301v3
• Date: Sun, 8 Sep 2024 17:44:14 GMT
• Title: Estimating Conditional Mutual Information for Dynamic Feature Selection
• Authors: Soham Gadgil, Ian Covert, Su-In Lee,
• Abstract summary: Dynamic feature selection is a promising paradigm to reduce feature acquisition costs and provide transparency into a model's predictions. Here, we take an information-theoretic perspective and prioritize features based on their mutual information with the response variable. Our method provides consistent gains over recent methods across a variety of datasets.
• Score: 14.706269510726356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic feature selection, where we sequentially query features to make accurate predictions with a minimal budget, is a promising paradigm to reduce feature acquisition costs and provide transparency into a model's predictions. The problem is challenging, however, as it requires both predicting with arbitrary feature sets and learning a policy to identify valuable selections. Here, we take an information-theoretic perspective and prioritize features based on their mutual information with the response variable. The main challenge is implementing this policy, and we design a new approach that estimates the mutual information in a discriminative rather than generative fashion. Building on our approach, we then introduce several further improvements: allowing variable feature budgets across samples, enabling non-uniform feature costs, incorporating prior information, and exploring modern architectures to handle partial inputs. Our experiments show that our method provides consistent gains over recent methods across a variety of datasets.

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