Performance evaluation of the machine learning algorithms for emotion classification on the CASE dataset

Abstract

Emotion classification using physiological signals is still a challenging task even the sensor technology and machine learning algorithms evolved within the decades. In this study, the performance of KNN, DT, RF, LR, and XGB algorithms on emotion classification was evaluated in terms of accuracy on the CASE dataset. Three sub-datasets namely Downsampled, Resampled-EM, and Resampled-VA were obtained from the original dataset. Then, hyperparameter tuning was applied to the smallest dataset and the algorithms were applied with the parameters that were obtained in hyperparameter tuning to the Resampled-EM, Resampled-VA, and original sets. As the results obtained, KNN, RF, and XGB provided higher accuracies on the Resampled-VA set when compared to the Resampled-EM set, where it was the contrary for the DT algorithm. XGB algorithm provided the highest accuracy of 97.44% among all the results. This study could be considered as the most comprehensive study that utilizes machine learning algorithms for emotion classification on the CASE dataset.