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Article · Computers · April 20, 2026

A Reproducible Hybrid Architecture of Fuzzy Logic and XGBoost for Explainable Tabular Classification of Territorial Vulnerability

Aiman Akynbekova, Ayagoz Mukhanova, Raikhan Muratkhan, Lunara DIYAROVA, Saya BAIGUBENOVA, Gulden Murzabekova

Zhanat Abdikadyr
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Abstract

This study proposes a reproducible hybrid computational model for the explainable classification of territorial vulnerability using heterogeneous tabular data. The approach integrates fuzzy logic and extreme gradient boosting in a two-stage architecture that balances interpretability and predictive performance. First, a fuzzy transformation is applied to construct interpretable risk and resilience indicators based on multi-source administrative indicators. The analytical dataset was formed by integrating 11 heterogeneous administrative sources into a single matrix of 166 territorial units and 76 features. The model was evaluated on a stratified 75/25 split of the training and test sets using the F1 score, ROC-AUC, precision, recall, and integrated quality criterion. Experimental results show that the proposed Fuzzy-XGBoost framework achieved an F1 score of 0.7333 on the test dataset, an ROC-AUC of 0.8291, and an Integrated Score of 0.768, outperforming the strongest baseline and improving recall in highly vulnerable areas. Furthermore, probabilistic threshold optimization identified an operating point at τ = 0.35, reducing the number of missed high-risk cases while maintaining acceptable specificity. The results demonstrate that fuzzy feature expansion combined with gradient boosting provides an efficient and interpretable solution for tabular risk classification and decision support problems under heterogeneity and uncertainty.

Explainable Artificial Intelligence (XAI)Imbalanced Data Classification TechniquesData Mining and Machine Learning Applicationsterritorial vulnerabilityfuzzy logicXGBoost

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