Home > Archive > 2020 > Volume 10 Number 6 (Dec. 2020) >
IJMLC 2020 Vol.10(6): 735-739 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.6.998

Homogeneous Ensemble Instance Intervals Determination Method of Time Series Data Based on Granular Computing

Jaewoong Kang, Wooseong Yang, and Mye Sohn

Abstract—It is very important to determine the size of the instance since it has a large impact on the recognition performance of the devices. In this paper, we propose a novel method to recognize the intervals of the time-series data using granular computing. Unlike traditional methods which use fixed size or knowledge-based, our method is conducted data-driven. Based on the concept of the granular computing, we classified the operation data of devices into three levels and proposed a multi-SVM-based machine learning method that can automatically classify each granule. We have proven the excellence of our method by conducting and evaluating experiments with two perspectives.

Index Terms—Feature selection, granular computing, instance interval, time-series data.

Jaewoong Kang, Wooseong Yang, and Mye Sohn are with the Sungkyunkwan University, Suwon, Korea (e-mail: {kjw1727, yus0363, myesohn}@skku.edu).

[PDF]

Cite: Jaewoong Kang, Wooseong Yang, and Mye Sohn, "Homogeneous Ensemble Instance Intervals Determination Method of Time Series Data Based on Granular Computing," International Journal of Machine Learning and Computing vol. 10, no. 6, pp. 735-739, 2020.

Copyright © 2020 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


Article Metrics