MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest
This work addresses the need for explainability in security-critical machine learning applications, though it appears incremental as it builds on existing formal methods for a specific model type.
The authors tackled the problem of explainability in Random Forest models by developing a novel method based on Minimal Unsatisfiable Cores (MUC) for feature importance measurement and adversarial analysis, achieving high-quality results and outperforming state-of-the-art methods in adversarial analysis.
The broad adoption of Machine Learning (ML) in security-critical fields demands the explainability of the approach. However, the research on understanding ML models, such as Random Forest (RF), is still in its infant stage. In this work, we leverage formal methods and logical reasoning to develop a novel model-specific method for explaining the prediction of RF. Our approach is centered around Minimal Unsatisfiable Cores (MUC) and provides a comprehensive solution for feature importance, covering local and global aspects, and adversarial sample analysis. Experimental results on several datasets illustrate the high quality of our feature importance measurement. We also demonstrate that our adversarial analysis outperforms the state-of-the-art method. Moreover, our method can produce a user-centered report, which helps provide recommendations in real-life applications.