An Ensemble Learning-Based Method for Missing Data Imputation in Road Maintenance

To improve the accuracy of imputing missing maintenance engineering data, an ensemble learning method integrating three algorithms—XGBoost, Support Vector Machine (SVM), and Multilayer Perceptron (MLP)—is proposed. This method constructs three base learners (XGBoost, SVM, and MLP) to establish the nonlinear mapping relationship between maintenance measures and multi-dimensional influencing factors through supervised learning. A soft voting ensemble strategy based on probability weighting is adopted to optimize the comprehensive decision-making effect of the model output, and the imputation performance of each model is systematically evaluated. The research results show that the proposed ensemble learning method achieves an accuracy of 99% in missing data imputation, which is significantly superior to the single models MLP (54%), SVM (72%), and XGBoost (77%). This verifies the effectiveness and superiority of the method in imputing missing maintenance data.