Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach

Abstract

The features present in large datasets significantly affect the performance of machine learning models. Redundant and irrelevant features will be rejected and cause a decrease in machine learning model performance. This paper proposes HyFeS-ROS-ANN: Hybrid Feature Selection and Resampling combination method for binary classification using artificial neural network multilayer perceptron (MLP). The first stage of this approach is to use a combination of two feature selection methods to select essential features that are highly correlated with model performance. The second stage of this approach is to use a combination of resampling methods to handle unbalanced data classes. Both approaches are applied to the academic performance classification model using the MLP neural network. This research dataset is obtained using three-dimensional (3D) frameworks such as the Big Five Personality to determine the Personality that affects academic performance from the student dimension, the Family Influence Scale (FIS), which measures factors that affect academic performance from the family dimension, and Higher Education Institutions Service Quality (HEISQUAL) to measure service quality and its influence on academic performance from the Education institution dimension. Previous research shows that the CoR-ANN algorithm has a model accuracy rate of 94%. The research results based on the dataset show that our proposed method can improve accuracy by selecting more relevant and essential features in improving model performance. The results show that the features are reduced from 135 to 108, while the HyFS-ROS-ANN model for binary classification accuracy increases to 100%.