Abstract—This investigation tried to compare the features
that influence vehicle style with Chi-square test and the
representative decision tree method. This study has three
outcomes. First of all, the investigation using Chi-square test
could find evidence of the correlation between car style and
some design features. Secondly, for the goal to improve
accuracy, this investigation created a method of the
representative decision tree. It built 50 decision trees to
calculate accuracy to compare and to choose the best one tree.
The third, although vehicle style was related to some design
features, there were still differences in the chosen design
features between the representative decision tree method and
Chi-square test. The ranking of importance for the design
features correlate with the vehicle style was not the same.
Finally, we attempted to use design knowledge in this study to
create a series of 3d modeling concepts with different vehicle
Index Terms—Representative decision tree, chi-square test, design features, vehicle style.
Hung-Hsiang Wang is with the Department of Industrial Design at National Taipei University of Technology, Taipei, Taiwan (e-mail: email@example.com).
Chip-Ping Chen is with the College of Design at National Taipei University of Technology. Taipei, Taiwan (Corresponding author; e-mail: firstname.lastname@example.org).
Cite: Hung-Hsiang Wang and Chih-Ping Chen, "Comparison of Chi-Square Test and Representative Decision Tree in Features that Influence Vehicle Style," International Journal of Machine Learning and Computing vol. 11, no. 5, pp. 333-338, 2021.Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).