Deriving the relationship between user satisfaction on engine sounds and affective variable sets based on classification algorithms

This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defined by three axes, and two classification algorithms were used to determine the pred...

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Bibliographic Details
Published in2014 IEEE International Conference on Industrial Engineering and Engineering Management pp. 1310 - 1313
Main Authors Kim, W. J., Kim, G. W., Lee, Y. S., Yun, M. H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Accuracy
classification algorithm
Classification algorithms
Emotion prediction
engine sounds
Engines
Logistics
Neurons
Prediction algorithms
product sounds
user satisfaction
Vehicles
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Summary:This study aims to extract the most relevant set consisted of affective variables to the level of user satisfaction on engine sounds using classification algorithm. The affective variables for engine sounds were defined by three axes, and two classification algorithms were used to determine the prediction accuracy for those affective axes. The study was consisted of three phases: 1) extracting sets of affective variables and the level of satisfaction on engine sounds, 2) preprocessing of engine sounds and experiment design, and 3) analysis of the most relevant sets of affective variables to user satisfaction. As a result, PA (Powerful-Affective) variable set showed the highest prediction accuracy of user satisfaction compared to other sets. Predicting the level of satisfaction based on classification algorithm could help to generalize the relationship between user satisfaction and affective variables more easily, beyond the limitation with a small size of subjects.
ISSN:2157-3611
DOI:10.1109/IEEM.2014.7058850