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

• URL: http://arxiv.org/abs/2504.09733v2
• Date: Sat, 30 Aug 2025 23:37:51 GMT
• 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,
• Abstract summary: Accurately estimating decision boundaries in black box systems is critical when ensuring safety, quality, and feasibility in real-world applications.<n>This paper presents $varepsilon$-Neighborhood Decision-Boundary Governed Estimation (EDGE), a sample efficient and function-agnostic algorithm that estimates the location of the decision boundary of a black box binary classifier within a user-specified $varepsilon$-neighborhood.<n> Evaluations are conducted on three test functions, where it is seen that the EDGE algorithm demonstrates superior sample efficiency and better boundary approximation than adaptive sampling techniques and grid-based searches.
• Score: 1.2647816797166167
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Evaluations are conducted on three test functions, where it is seen that the EDGE algorithm demonstrates superior sample efficiency and better boundary approximation than adaptive sampling techniques and grid-based searches.

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