论文标题
MUC驱动的特征重要性测量和对对抗性分析的随机森林
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest
论文作者
论文摘要
机器学习（ML）在关键领域的广泛采用需要该方法的解释性。但是，了解ML模型（例如随机森林（RF））的研究仍处于婴儿阶段。在这项工作中，我们利用形式方法和逻辑推理来开发一种新颖的模型特异性方法来解释RF的预测。我们的方法围绕最小的核心（MUC），并为特征重要性提供了全面的解决方案，涵盖了本地和全球方面以及对抗性样本分析。几个数据集的实验结果说明了我们特征重要性测量的高质量。我们还证明，对抗性分析的表现优于最新方法。此外，我们的方法可以产生以用户为中心的报告，这有助于在现实生活中提供建议。
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.
