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Research on Dynamic Hyperparameter Optimization Algorithm for University Financial Risk Early Warning Based on Multi-Objective Bayesian Optimization
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DOI：10.3390/forecast7040061.png)
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Abstract
En 中文
Financial sustainability in higher education is increasingly fragile due to policy shifts, rising costs, and funding volatility. Legacy early-warning systems based on static thresholds or rules struggle to adapt to these dynamics and often overlook fairness and interpretability-two essentials in public-sector governance. We propose a university financial risk early-warning framework that couples a causal-attention Transformer with Multi-Objective Bayesian Optimization (MBO). The optimizer searches a constrained Pareto frontier to jointly improve predictive accuracy (AUC up arrow), fairness (demographic parity gap, DP_Gap down arrow), and computational efficiency (time down arrow). A sparse kernel surrogate (SKO) accelerates convergence in high-dimensional tuning; a dual-head output (risk probability and health score) and SHAP-based attribution enhance transparency and regulatory alignment. On multi-year, multi-institution data, the approach surpasses mainstream baselines in AUC, reduces DP_Gap, and yields expert-consistent explanations. Methodologically, the design aligns with LLM-style time-series forecasting by exploiting causal masking and long-range dependencies while providing governance-oriented explainability. The framework delivers earlier, data-driven signals of financial stress, supporting proactive resource allocation, funding restructuring, and long-term planning in higher education finance.
Keywords:
financial risk early warning
Multi-Objective Bayesian Optimization
causal attention transformer
fairness-aware learning
interpretability (SHAP analysis)
sparse kernel surrogate models
dynamic hyperparameter tuning
Journal
F
IF:
3.2
Papers:
62
Citations:
0
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