Green Transportation Financial Risk Early Warning Based on Data Dimensionality Reduction and Improved PSO Optimization of SVM under the Background of Low-Carbon Development
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Abstract
Reliable early-warning systems in complex information environments require efficient feature extraction, adaptive parameter optimization, and accurate classification of high-dimensional data. This study proposes a data-driven intelligent warning framework integrating principal component analysis (PCA), an improved particle swarm optimization (PSO) algorithm, and support vector machines (SVM). PCA is employed to suppress feature redundancy and construct compact representations of multidimensional information, thereby improving computational efficiency and model robustness. To enhance classification performance, an improved PSO strategy incorporating adaptive inertia weighting, chaotic initialization, and mutation mechanisms is developed for global optimization of SVM parameters. Furthermore, a multi-source indicator architecture is established by integrating financial, environmental, policysensitive, and market-related attributes into a unified evaluation framework. Experimental results demonstrate that the proposed model achieves a classification accuracy of 94.2%, outperforming conventional SVM and random-forest approaches while reducing training complexity. The proposed framework establishes an effective methodology for high -dimensional feature extraction, intelligent signal classification, and adaptive optimization, providing a practical solution for monitoring, anomaly detection, and decision-support applications in complex dynamic systems.
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