TY - GEN
T1 - Feature expansion of single dimensional time series data for machine learning classification
AU - Jung, Daeun
AU - Lee, Jungjin
AU - Park, Hyunggon
N1 - Funding Information:
This work was supported in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00024, Supervised Agile Machine Learning Techniques for Network Automation based on Network Data Analytics Function, No. 2021-0-00739, Development of Distributed/Cooperative AI based 5G+ Network Data Analytics Functions and Control Technology) and supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)(No. NRF-2020R1A2B5B01002528).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/17
Y1 - 2021/8/17
N2 - 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.
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.
KW - biosensor data
KW - feature expansion
KW - machine learning
KW - time series data
UR - http://www.scopus.com/inward/record.url?scp=85115615781&partnerID=8YFLogxK
U2 - 10.1109/ICUFN49451.2021.9528690
DO - 10.1109/ICUFN49451.2021.9528690
M3 - Conference contribution
AN - SCOPUS:85115615781
T3 - International Conference on Ubiquitous and Future Networks, ICUFN
SP - 96
EP - 98
BT - ICUFN 2021 - 2021 12th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
T2 - 12th International Conference on Ubiquitous and Future Networks, ICUFN 2021
Y2 - 17 August 2021 through 20 August 2021
ER -