被撤回的 arXiv 论文：Surrogate Graph Partitioning for Spatial Prediction

This paper has been withdrawn by Yuta Shikuri

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Abstract:Spatial prediction refers to the estimation of unobserved values from spatially distributed observations. Although recent advances have improved the capacity to model diverse observation types, adoption in practice remains limited in industries that demand interpretability. To mitigate this gap, surrogate models that explain black-box predictors provide a promising path toward interpretable decision making. In this study, we propose a graph partitioning problem to construct spatial segments that minimize the sum of within-segment variances of individual predictions. The assignment of data points to segments can be formulated as a mixed-integer quadratic programming problem. While this formulation potentially enables the identification of exact segments, its computational complexity becomes prohibitive as the number of data points increases. Motivated by this challenge, we develop an approximation scheme that leverages the structural properties of graph partitioning. Experimental results demonstrate the computational efficiency of this approximation in identifying spatial segments.

Comments:	The authors have mutually agreed to withdraw this manuscript, as they no longer intend to pursue it as a joint work. The subsequent research will be pursued under a different authorship arrangement
Subjects:	Machine Learning (stat.ML); Machine Learning (cs.LG)
Cite as:	arXiv:2510.07832 [stat.ML]
	(or arXiv:2510.07832v2 [stat.ML] for this version)
	https://doi.org/10.48550/arXiv.2510.07832 arXiv-issued DOI via DataCite

Submission history

From: Yuta Shikuri [view email]
[v1] Thu, 9 Oct 2025 06:24:49 UTC (1,806 KB)
[v2] Fri, 9 Oct 2026 13:46:05 UTC (1 KB) (withdrawn)