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

Daeun Jung; Jungjin Lee; Hyunggon Park

doi:10.1109/ICUFN49451.2021.9528690

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

Daeun Jung, Jungjin Lee, Hyunggon Park

Electronic and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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 language	English
Title of host publication	ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks
Publisher	IEEE Computer Society
Pages	96-98
Number of pages	3
ISBN (Electronic)	9781728164762
DOIs	https://doi.org/10.1109/ICUFN49451.2021.9528690
State	Published - 17 Aug 2021
Event	12th International Conference on Ubiquitous and Future Networks, ICUFN 2021 - Virtual, Jeju Island, Korea, Republic of Duration: 17 Aug 2021 → 20 Aug 2021

Publication series

Name	International Conference on Ubiquitous and Future Networks, ICUFN
Volume	2021-August
ISSN (Print)	2165-8528
ISSN (Electronic)	2165-8536

Conference

Conference	12th International Conference on Ubiquitous and Future Networks, ICUFN 2021
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	17/08/21 → 20/08/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

biosensor data
feature expansion
machine learning
time series data

Access to Document

10.1109/ICUFN49451.2021.9528690

Cite this

Jung, D., Lee, J., & Park, H. (2021). Feature expansion of single dimensional time series data for machine learning classification. In ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks (pp. 96-98). (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2021-August). IEEE Computer Society. https://doi.org/10.1109/ICUFN49451.2021.9528690

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title = "Feature expansion of single dimensional time series data for machine learning classification",

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.",

keywords = "biosensor data, feature expansion, machine learning, time series data",

author = "Daeun Jung and Jungjin Lee and Hyunggon Park",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th International Conference on Ubiquitous and Future Networks, ICUFN 2021 ; Conference date: 17-08-2021 Through 20-08-2021",

year = "2021",

month = aug,

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doi = "10.1109/ICUFN49451.2021.9528690",

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Jung, D, Lee, J & Park, H 2021, Feature expansion of single dimensional time series data for machine learning classification. in ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks. International Conference on Ubiquitous and Future Networks, ICUFN, vol. 2021-August, IEEE Computer Society, pp. 96-98, 12th International Conference on Ubiquitous and Future Networks, ICUFN 2021, Virtual, Jeju Island, Korea, Republic of, 17/08/21. https://doi.org/10.1109/ICUFN49451.2021.9528690

Feature expansion of single dimensional time series data for machine learning classification. / Jung, Daeun; Lee, Jungjin; Park, Hyunggon.
ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks. IEEE Computer Society, 2021. p. 96-98 (International Conference on Ubiquitous and Future Networks, ICUFN; Vol. 2021-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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

Abstract

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Bibliographical note

Keywords

Access to Document

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