Abstract

This study presents an innovative approach to support educational administration, focusing on the optimization of university admission processes using feature selection algorithms. The research addresses the challenges of concept drift, outlier treatment, and the weighting of key factors in admission criteria. The proposed methodology identifies the optimal set of features and assigns weights to the selection criteria that demonstrate the strongest correlation with academic performance, thereby contributing to improved educational management by optimizing decision-making processes. The approach incorporates concept change management and outlier detection in the preprocessing stage while employing multivariate feature selection techniques in the processing stage. Applied to the admission process of engineering students at a public Chilean university, the methodology considers socioeconomic, academic, and demographic variables, with curricular advancement as the objective. The process generated a subset of attributes and an application score with predictive capabilities of 83% and 84%, respectively. The results show a significantly greater association between the application score and academic performance when the methodology's weights are used, compared to the actual weights. This highlights the increased predictive power by accounting for concept drift, outliers, and shared information between variables.