标题： 二维黑盒分类器函数的基于ε-邻域决策边界的估计（EDGE） 显示英文标题

标题： Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE) of 2D Black Box Classifier Functions

摘要： 在确保实际应用中的安全性、质量和可行性时，准确估计黑盒系统的决策边界至关重要。 然而，现有的方法通过在不确定性区域采样来迭代地改进边界估计，但无法保证接近决策边界的程度，并且会导致不必要的探索，特别是在评估成本高昂的情况下尤为不利。 本文提出了 **Epsilon-邻域决策边界引导估计（EDGE）算法**，这是一种样本高效且与函数无关的算法，利用中间值定理在用户指定的 epsilon-邻域内估算黑盒二元分类器的决策边界位置。 该算法在三个非线性测试函数和一个具有不确定可再生能源注入的电力系统稳定性问题案例研究中进行了评估。 结果显示，与自适应采样技术和基于网格的搜索相比，EDGE 算法表现出更高的样本效率和更优的边界逼近能力。 显示英文摘要

摘要： Accurately estimating decision boundaries in black box systems is critical when ensuring safety, quality, and feasibility in real-world applications. However, existing methods iteratively refine boundary estimates by sampling in regions of uncertainty, without providing guarantees on the closeness to the decision boundary and also result in unnecessary exploration that is especially disadvantageous when evaluations are costly. This paper presents the Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE), a sample efficient and function-agnostic algorithm that leverages the intermediate value theorem to estimate the location of the decision boundary of a black box binary classifier within a user-specified epsilon-neighborhood. Evaluations are conducted on three nonlinear test functions and a case study of an electric grid stability problem with uncertain renewable power injection. The EDGE algorithm demonstrates superior sample efficiency and better boundary approximation than adaptive sampling techniques and grid-based searches.