Applying LCS to affective image classification in spatial-frequency domain

Affective image classification is a task aims on classifying images based on their affective characteristics of inducing human emotions. This study achieves the task by using Learning Classifier System (LCS) and spatial-frequency features. The model built by using LCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the LCS is compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used in classification tasks. The study presents user-independent results which indicate that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation.