mlsanityData to 14 Oct 2026

SX-GeoTree: Self-eXplaining Geospatial Regression Tree Incorporating the Spatial Similarity of Feature Attributions

arXiv 2511.19845 ↗First posted 25 Nov 2025cs.LG cs.CY stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Moran's I), and explanation robustness via modularity maximization on a consensus similarity network formed from (a) geographically weighted regression (GWR) coefficient distances (stimulus-response similarity) and (b) SHAP attribution distances (explanatory similarity). We recast local Lipschitz continuity of feature attributions as a network community preservation problem, enabling scalable enforcement of spatially coherent explanations without per-sample neighborhood searches. Experiments on two exemplar tasks (county-level GDP in Fujian, n=83; point-wise housing prices in Seattle, n=21,613) show SX-GeoTree maintains competitive predictive accuracy (within 0.01 R2 of decision trees) while improving residual spatial evenness and doubling attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05). Ablation confirms Moran's I and modularity terms are complementary; removing either degrades both spatial residual structure and explanation stability. The framework demonstrates how spatial similarity - extended beyond geometric proximity through GWR-derived local relationships - can be embedded in interpretable models, advancing trustworthy geospatial machine learning and offering a transferable template for domain-aware explainability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 56 references that are in the map. First posted on arXiv 25 Nov 2025.

Builds on

20 of 56 references.

References · 20

  1. 2018Reconciling modern machine learning practice and the bias-variance trade-offcited by 927
  2. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  3. 2018Towards Robust Interpretability with Self-Explaining Neural Networkscited by 505
  4. 2018Regularisation of Neural Networks by Enforcing Lipschitz Continuitycited by 314
  5. 2018On the Robustness of Interpretability Methodscited by 314
  6. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 285
  7. 2020A Survey on the Explainability of Supervised Machine Learningcited by 227
  8. 2019The Shapley Taylor Interaction Indexcited by 143
  9. 2018Deep Neural Decision Treescited by 72
  10. 2022Faith-Shap: The Faithful Shapley Interaction Indexcited by 69
  11. 2024Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensemblescited by 31
  12. 2019The Theory Behind Overfitting, Cross Validation, Regularization, Bagging, and Boosting: Tutorialcited by 22
8 more
  1. 2020Self-explaining AI as an alternative to interpretable AIcited by 18
  2. 2023Learning by Self-Explainingcited by 17
  3. 2021Joint Shapley values: a measure of joint feature importancecited by 15
  4. 2024Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibriumcited by 9
  5. 2023GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Modelscited by 5
  6. 2023Shapley Sets: Feature Attribution via Recursive Function Decompositioncited by 2
  7. 2025Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Studycited by 2
  8. 2025Learning Pareto manifolds in high dimensions: How can regularization help?cited by 1

Built on by

Not cited yet.