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• W2142305616 abstract "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." @default.
• W2142305616 created "2016-06-24" @default.
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• W2142305616 date "2014-07-01" @default.
• W2142305616 modified "2023-09-23" @default.
• W2142305616 title "Applying LCS to affective image classification in spatial-frequency domain" @default.
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• W2142305616 doi "https://doi.org/10.1109/cec.2014.6900620" @default.
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