Feature expansion of single dimensional time series data for machine learning classification

Daeun Jung, Jungjin Lee, Hyunggon Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a feature expansion approach for the lowest one-dimension (1-D) time series data classification problems, where the expanded features include temporal, frequency, and statistical characteristics. We show that the proposed feature expansion can improve the classification accuracy compared to conventional machine learning algorithms for data classification. This is because the expanded features enable classifiers to consider multiple dimensions which are not feasible for low dimension data. Experiment results show that the proposed feature expansion method can improve the classification performance compared to conventional machine learning algorithms for 1-D actual biosensor data.

Original languageEnglish
Title of host publicationICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks
PublisherIEEE Computer Society
Pages96-98
Number of pages3
ISBN (Electronic)9781728164762
DOIs
StatePublished - 17 Aug 2021
Event12th International Conference on Ubiquitous and Future Networks, ICUFN 2021 - Virtual, Jeju Island, Korea, Republic of
Duration: 17 Aug 202120 Aug 2021

Publication series

NameInternational Conference on Ubiquitous and Future Networks, ICUFN
Volume2021-August
ISSN (Print)2165-8528
ISSN (Electronic)2165-8536

Conference

Conference12th International Conference on Ubiquitous and Future Networks, ICUFN 2021
Country/TerritoryKorea, Republic of
CityVirtual, Jeju Island
Period17/08/2120/08/21

Keywords

  • biosensor data
  • feature expansion
  • machine learning
  • time series data

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