University Rankings Prediction Using Hybrid Feature Selection Based on Machine Learning Methods

This study presents a novel approach to predicting university rankings using hybrid feature selection and machine learning techniques. It identifies critical performance factors that affect ranking accuracy using the Times Higher Education (THE) dataset, which includes data from 1,904 universities. A Max-Min normalization method and an artificial neural network were applied to preprocess the data. Then, a hybrid feature selection method, combining statistical and machine learning techniques, was used to determine the optimal feature subsets. Several prediction models, including linear regression, random forest, and multilayer perceptron, were evaluated and compared based on various metrics: accuracy, precision, mean absolute error (MAE), root mean square error (RMSE), and R². The results indicate that hybrid feature selection using machine learning significantly enhances predictive accuracy. The hybrid model consistently outperformed all other models across various metrics, achieving the highest accuracy (0.971), precision (0.985), recall (0.971), and F1-score (0.972). These results demonstrate that the hybrid model effectively balances true positive and false positive predictions while minimizing errors. Furthermore, the error metrics for the hybrid model were the lowest among all models, with an MAE of 0.034 and an RMSE of 0.028. This reinforces its superiority in delivering highly reliable predictions. This study demonstrates the effectiveness of hybrid feature selection in refining ranking systems and offers a robust framework that can be applied to various datasets and ranking environments. The findings provide valuable insights for improving ranking predictions and shaping strategies in higher education.