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IJMLC 2021 Vol.11(5): 333-338 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.5.1057

Comparison of Chi-Square Test and Representative Decision Tree in Features that Influence Vehicle Style

Hung-Hsiang Wang and Chih-Ping Chen

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 design styles.

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: wanghh@mail.ntut.edu.tw).
Chip-Ping Chen is with the College of Design at National Taipei University of Technology. Taipei, Taiwan (Corresponding author; e-mail: roychen092@hotmail.com).


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).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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