Multiclass Classification Method in Handheld Based Smartphone Gait Identification

Authors

  • Abdul Rafiez Abdul Raziff Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Md Nasir Sulaiman Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Norwati Mustapha Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Thinagaran Perumal Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
  • Muhammad Syafiq Mohd Pozi Faculty of Computer Science and Engineering, Kyoto Sangyo University Motoyama, Kamigamo, Kita-ku, Kyoto-City 603-8555, Japan

Keywords:

Gait Identification, Multiclass Classification, OvA, OvO, RCC, Single Classifier,

Abstract

Gait identification has been widely used in many types of research and application. Since gait identification involves with many people and classes, using a single classifier is not a good option as the dataset may contains overlapped class boundary and moreover, most of the classifiers are well built for binary classes. This paper discusses the application of multiclass classifiers such as one-vs-all (OvA), one-vs-one (OvO) and random correction code (RCC) on handheld based smartphone gait signal for person identification. The mapping uses J48 as the main classifier. The result is then compared with a single J48 for the benchmark. Finally, the best multiclass method is compared with few machine learning classifier in-order to see its capability. From the result, it can be seen that using OvO and RCC thus increase the accuracy performance if compared to a single classifier. For the best classifier in the multiclass mapping method, it can be seen that J48 yield the best accuracy score.

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Published

2017-09-15

How to Cite

Abdul Raziff, A. R., Sulaiman, M. N., Mustapha, N., Perumal, T., & Mohd Pozi, M. S. (2017). Multiclass Classification Method in Handheld Based Smartphone Gait Identification. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-12), 59–65. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2771

Issue

Vol. 9 No. 2-12: The Challenge of Computer and Engineering in Building Sustainable Service Systems II

Section

Articles

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