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[2504.09733] Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE) of 2D Black Box Classifier Functions
Summary
The paper presents the Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE) algorithm for efficiently estimating decision boundaries in black box classifiers, demonstrating superior performance in sample efficiency and boundary approximation.
Why It Matters
Accurately estimating decision boundaries in black box systems is crucial for applications in safety and quality assurance. The EDGE algorithm addresses limitations of existing methods, providing a more efficient approach that can reduce costs in scenarios where evaluations are expensive.
Key Takeaways
- EDGE algorithm improves decision boundary estimation in black box classifiers.
- Utilizes the intermediate value theorem for enhanced accuracy.
- Demonstrates better sample efficiency compared to traditional methods.
- Applicable in real-world scenarios, such as electric grid stability.
- Reduces unnecessary exploration in costly evaluation environments.
Computer Science > Computational Geometry arXiv:2504.09733 (cs) [Submitted on 13 Apr 2025 (v1), last revised 13 Feb 2026 (this version, v3)] Title:Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE) of 2D Black Box Classifier Functions Authors:Mithun Goutham, Riccardo DalferroNucci, Stephanie Stockar, Meghna Menon, Sneha Nayak, Harshad Zade, Chetan Patel, Mario Santillo View a PDF of the paper titled Epsilon-Neighborhood Decision-Boundary Governed Estimation (EDGE) of 2D Black Box Classifier Functions, by Mithun Goutham and 7 other authors View PDF HTML (experimental) Abstract: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 $\varepsilon$-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 $\varepsilon$-neighborhood. To demonstrate applicability, a case study is presented of an electric grid stability problem with uncertain renewable power injection. Evaluat...
