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Prediction of Future Mood Using Majority Vote Based on Certainty Factor
Yusuke Kajiwara, Shinya Yonekura and Haruhiko Kimura
(Received October 17, 2017; Accepted November 28, 2017)
Keywords: prediction of the future mood, circumplex model of affect, certainty factor, majority vote
When students feel depressed, their performance will decline, and they will not attend the university. To prevent non-attendance in the university, we propose a system of mood prediction using the majority vote based on a certainty factor (MVCF). When part of the data cannot be obtained, MVCF predicts the future mood from available data. Moreover, MVCF predicts four types of mood namely excitement, relaxedness, depression, and nervousness. Experimental results show that MVCF can predict moods from tomorrow until two weeks with 0.7±0.1 accuracy. We clarified that the weather and scheduled events contribute to predicting the future mood. MVCF predicts more types of mood than the existing system. Moreover, the accuracy of MVCF is equal to that of the existing system.
Corresponding author: Yusuke Kajiwara
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