Lunara Diyarova
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Lunara Diyarova has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Aiman Akynbekova
Department of Information Systems, Lev Nikolaevich Gumilyov Eurasian National University, Astana 010000, Kazakhstan
1 shared publication - Aliya Satybaldieva
Department of Physics and Computer Science, Taraz University Named After Muhammad Khaydar Dulati, Taraz 010008, Kazakhstan
1 shared publication - Ayagoz Mukhanova
L.N. Gumilyov Eurasian National University
1 shared publication - Gulaim Orazymbetova
Department of Physics and Computer Science, Taraz University Named After Muhammad Khaydar Dulati, Taraz 010008, Kazakhstan
1 shared publication - Gulden Murzabekova
Department of Computer Sciences, Saken Seifullin Kazakh Agrotechnical Research University, Astana 010000, Kazakhstan
1 shared publication - Raikhan Muratkhan
Karaganda University named after Academician E.A. Buketov, Karaganda, Kazakhstan
1 shared publication
Latest Publications
- Open Access
A Reproducible Hybrid Architecture of Fuzzy Logic and XGBoost for Explainable Tabular Classification of Territorial Vulnerability
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.
Open AccessA Reproducible Hybrid Architecture of Fuzzy Logic and XGBoost for Explainable Tabular Classification of Territorial Vulnerability
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.