Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction

Yohan Joo; Jongpil Jeong

doi:10.1007/978-3-030-24311-1_5

Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction

Yohan Joo
, Jongpil Jeong

Research & Business Foundation

Sungkyunkwan University

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

3 Scopus citations

Abstract

To predict for machine defects, a classifier is required to classify the time series data collected from the sensors into the fault state and the normal state. In many cases, the data collected by sensors is time series data collected at various frequencies. Excessive computer load is required to handle this as it is. Therefore, there has been a lot of research being done on the process of extracting features that are highly classified from time series data. In particular, data generated at real-world is unbalanced and noisy, requiring time series classifiers to minimize their impact. Shapelet transformation is generally effectively known for classifying time series data. This paper proposes a process of feature extraction that is strong for noise and over-fitting to be applicable in practice. We can extract the feature from the time series data through the proposed algorithm and expect it to be used in various fields such as smart factory.

Original language	English
Title of host publication	Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings
Editors	Sanjay Misra, Osvaldo Gervasi, Beniamino Murgante, Elena Stankova, Vladimir Korkhov, Carmelo Torre, Eufemia Tarantino, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan
Publisher	Springer Verlag
Pages	69-80
Number of pages	12
ISBN (Print)	9783030243104
DOIs	https://doi.org/10.1007/978-3-030-24311-1_5
State	Published - 2019
Event	19th International Conference on Computational Science and Its Applications, ICCSA 2019 - Saint Petersburg, Russian Federation Duration: 1 Jul 2019 → 4 Jul 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11624 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Computational Science and Its Applications, ICCSA 2019
Country/Territory	Russian Federation
City	Saint Petersburg
Period	1/07/19 → 4/07/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Adaboosting
Shapelet
Time series classification
Under Sampling

Access to Document

10.1007/978-3-030-24311-1_5

Cite this

Joo, Y., & Jeong, J. (2019). Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction. In S. Misra, O. Gervasi, B. Murgante, E. Stankova, V. Korkhov, C. Torre, E. Tarantino, A. M. A. C. Rocha, D. Taniar, & B. O. Apduhan (Eds.), Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings (pp. 69-80). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11624 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-24311-1_5

Joo, Yohan ; Jeong, Jongpil. / Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction. Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings. editor / Sanjay Misra ; Osvaldo Gervasi ; Beniamino Murgante ; Elena Stankova ; Vladimir Korkhov ; Carmelo Torre ; Eufemia Tarantino ; Ana Maria A.C. Rocha ; David Taniar ; Bernady O. Apduhan. Springer Verlag, 2019. pp. 69-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction",

abstract = "To predict for machine defects, a classifier is required to classify the time series data collected from the sensors into the fault state and the normal state. In many cases, the data collected by sensors is time series data collected at various frequencies. Excessive computer load is required to handle this as it is. Therefore, there has been a lot of research being done on the process of extracting features that are highly classified from time series data. In particular, data generated at real-world is unbalanced and noisy, requiring time series classifiers to minimize their impact. Shapelet transformation is generally effectively known for classifying time series data. This paper proposes a process of feature extraction that is strong for noise and over-fitting to be applicable in practice. We can extract the feature from the time series data through the proposed algorithm and expect it to be used in various fields such as smart factory.",

keywords = "Adaboosting, Shapelet, Time series classification, Under Sampling",

author = "Yohan Joo and Jongpil Jeong",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 19th International Conference on Computational Science and Its Applications, ICCSA 2019 ; Conference date: 01-07-2019 Through 04-07-2019",

year = "2019",

doi = "10.1007/978-3-030-24311-1\_5",

language = "English",

isbn = "9783030243104",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "69--80",

editor = "Sanjay Misra and Osvaldo Gervasi and Beniamino Murgante and Elena Stankova and Vladimir Korkhov and Carmelo Torre and Eufemia Tarantino and Rocha, \{Ana Maria A.C.\} and David Taniar and Apduhan, \{Bernady O.\}",

booktitle = "Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings",

}

Joo, Y & Jeong, J 2019, Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction. in S Misra, O Gervasi, B Murgante, E Stankova, V Korkhov, C Torre, E Tarantino, AMAC Rocha, D Taniar & BO Apduhan (eds), Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11624 LNCS, Springer Verlag, pp. 69-80, 19th International Conference on Computational Science and Its Applications, ICCSA 2019, Saint Petersburg, Russian Federation, 1/07/19. https://doi.org/10.1007/978-3-030-24311-1_5

Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction. / Joo, Yohan; Jeong, Jongpil.
Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings. ed. / Sanjay Misra; Osvaldo Gervasi; Beniamino Murgante; Elena Stankova; Vladimir Korkhov; Carmelo Torre; Eufemia Tarantino; Ana Maria A.C. Rocha; David Taniar; Bernady O. Apduhan. Springer Verlag, 2019. p. 69-80 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11624 LNCS).

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

TY - GEN

T1 - Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction

AU - Joo, Yohan

AU - Jeong, Jongpil

PY - 2019

Y1 - 2019

N2 - To predict for machine defects, a classifier is required to classify the time series data collected from the sensors into the fault state and the normal state. In many cases, the data collected by sensors is time series data collected at various frequencies. Excessive computer load is required to handle this as it is. Therefore, there has been a lot of research being done on the process of extracting features that are highly classified from time series data. In particular, data generated at real-world is unbalanced and noisy, requiring time series classifiers to minimize their impact. Shapelet transformation is generally effectively known for classifying time series data. This paper proposes a process of feature extraction that is strong for noise and over-fitting to be applicable in practice. We can extract the feature from the time series data through the proposed algorithm and expect it to be used in various fields such as smart factory.

AB - To predict for machine defects, a classifier is required to classify the time series data collected from the sensors into the fault state and the normal state. In many cases, the data collected by sensors is time series data collected at various frequencies. Excessive computer load is required to handle this as it is. Therefore, there has been a lot of research being done on the process of extracting features that are highly classified from time series data. In particular, data generated at real-world is unbalanced and noisy, requiring time series classifiers to minimize their impact. Shapelet transformation is generally effectively known for classifying time series data. This paper proposes a process of feature extraction that is strong for noise and over-fitting to be applicable in practice. We can extract the feature from the time series data through the proposed algorithm and expect it to be used in various fields such as smart factory.

KW - Adaboosting

KW - Shapelet

KW - Time series classification

KW - Under Sampling

UR - https://www.scopus.com/pages/publications/85068610432

U2 - 10.1007/978-3-030-24311-1_5

DO - 10.1007/978-3-030-24311-1_5

M3 - Conference contribution

AN - SCOPUS:85068610432

SN - 9783030243104

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 69

EP - 80

BT - Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings

A2 - Misra, Sanjay

A2 - Gervasi, Osvaldo

A2 - Murgante, Beniamino

A2 - Stankova, Elena

A2 - Korkhov, Vladimir

A2 - Torre, Carmelo

A2 - Tarantino, Eufemia

A2 - Rocha, Ana Maria A.C.

A2 - Taniar, David

A2 - Apduhan, Bernady O.

PB - Springer Verlag

T2 - 19th International Conference on Computational Science and Its Applications, ICCSA 2019

Y2 - 1 July 2019 through 4 July 2019

ER -

Joo Y, Jeong J. Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction. In Misra S, Gervasi O, Murgante B, Stankova E, Korkhov V, Torre C, Tarantino E, Rocha AMAC, Taniar D, Apduhan BO, editors, Computational Science and Its Applications - ICCSA 2019 - 19th International Conference, Proceedings. Springer Verlag. 2019. p. 69-80. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-24311-1_5

Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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