Optimizing Neural Networks for Academic Performance Classification Using Feature Selection and Resampling Approach
Didi Supriyadi,
Purwanto Purwanto,
Budi Warsito
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… Show more
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