An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness

Feng Zhu; Jianshe Feng; Zicheng Su; Min Xie

doi:10.1007/978-3-031-49413-0_86

An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness

Feng Zhu^*, Jianshe Feng, Zicheng Su, Min Xie

^*Corresponding author for this work

Department of Systems Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Data pre-processing is a crucial step for data management, communication, and modeling in Prognostics and Health Management (PHM) framework. However, most existing studies have primarily focused on feature selection while disregarding the significance of sample selection. In practice, effective sample selection not only decreases data redundancy but also enhances computational efficiency and model performance. This research introduces a new offline sample selection method that employs an optimization algorithm based on simultaneous sparse recovery. We formulate the sample selection problem as a linear programming model that is solvable using most standard convex solvers. The solution of the model not only identifies the important samples but also establishes the mapping relationships between each sample and the important ones. The method can identify a small subset of samples that are crucial by considering their usefulness and freshness. We demonstrate the effectiveness of our proposed method in a case study that employs the dataset from the 2016 PHM data challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Original language	English
Title of host publication	Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023)
Editors	Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
Place of Publication	Cham
Publisher	Springer
Pages	1119-1129
Volume	1
ISBN (Electronic)	978-3-031-49413-0
ISBN (Print)	9783031494123
DOIs	https://doi.org/10.1007/978-3-031-49413-0_86
Publication status	Published - 2024
Event	UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems (DAMAS 2023), International Conference on Maintenance Engineering (IncoME-V 2023), International conference on the Efficiency and Performance Engineering Network (TEPEN 2023) - University of Huddersfield, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sept 2023 https://tepen.net/tepen-conferences/

Publication series

Name	Mechanisms and Machine Science
Volume	151
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems (DAMAS 2023), International Conference on Maintenance Engineering (IncoME-V 2023), International conference on the Efficiency and Performance Engineering Network (TEPEN 2023)
Country/Territory	United Kingdom
City	Huddersfield
Period	29/08/23 → 1/09/23
Internet address	https://tepen.net/tepen-conferences/

Research Keywords

Data pre-processing
Optimization
Prognostics and health management
Sample selection
Virtual metrology

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10.1007/978-3-031-49413-0_86

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Zhu, F., Feng, J., Su, Z., & Xie, M. (2024). An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness. In A. D. Ball, H. Ouyang, J. K. Sinha, & Z. Wang (Eds.), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) (Vol. 1, pp. 1119-1129). (Mechanisms and Machine Science; Vol. 151). Springer . https://doi.org/10.1007/978-3-031-49413-0_86

Zhu, Feng ; Feng, Jianshe ; Su, Zicheng et al. / An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness. Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023). editor / Andrew D. Ball ; Huajiang Ouyang ; Jyoti K. Sinha ; Zuolu Wang. Vol. 1 Cham : Springer , 2024. pp. 1119-1129 (Mechanisms and Machine Science).

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abstract = "Data pre-processing is a crucial step for data management, communication, and modeling in Prognostics and Health Management (PHM) framework. However, most existing studies have primarily focused on feature selection while disregarding the significance of sample selection. In practice, effective sample selection not only decreases data redundancy but also enhances computational efficiency and model performance. This research introduces a new offline sample selection method that employs an optimization algorithm based on simultaneous sparse recovery. We formulate the sample selection problem as a linear programming model that is solvable using most standard convex solvers. The solution of the model not only identifies the important samples but also establishes the mapping relationships between each sample and the important ones. The method can identify a small subset of samples that are crucial by considering their usefulness and freshness. We demonstrate the effectiveness of our proposed method in a case study that employs the dataset from the 2016 PHM data challenge. {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",

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note = "UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems (DAMAS 2023), International Conference on Maintenance Engineering (IncoME-V 2023), International conference on the Efficiency and Performance Engineering Network (TEPEN 2023) ; Conference date: 29-08-2023 Through 01-09-2023",

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Zhu, F, Feng, J, Su, Z & Xie, M 2024, An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness. in AD Ball, H Ouyang, JK Sinha & Z Wang (eds), Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023). vol. 1, Mechanisms and Machine Science, vol. 151, Springer , Cham, pp. 1119-1129, UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems (DAMAS 2023), International Conference on Maintenance Engineering (IncoME-V 2023), International conference on the Efficiency and Performance Engineering Network (TEPEN 2023), Huddersfield, United Kingdom, 29/08/23. https://doi.org/10.1007/978-3-031-49413-0_86

An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness. / Zhu, Feng; Feng, Jianshe; Su, Zicheng et al.
Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023). ed. / Andrew D. Ball; Huajiang Ouyang; Jyoti K. Sinha; Zuolu Wang. Vol. 1 Cham: Springer , 2024. p. 1119-1129 (Mechanisms and Machine Science; Vol. 151).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness

AU - Zhu, Feng

AU - Feng, Jianshe

AU - Su, Zicheng

AU - Xie, Min

PY - 2024

Y1 - 2024

N2 - Data pre-processing is a crucial step for data management, communication, and modeling in Prognostics and Health Management (PHM) framework. However, most existing studies have primarily focused on feature selection while disregarding the significance of sample selection. In practice, effective sample selection not only decreases data redundancy but also enhances computational efficiency and model performance. This research introduces a new offline sample selection method that employs an optimization algorithm based on simultaneous sparse recovery. We formulate the sample selection problem as a linear programming model that is solvable using most standard convex solvers. The solution of the model not only identifies the important samples but also establishes the mapping relationships between each sample and the important ones. The method can identify a small subset of samples that are crucial by considering their usefulness and freshness. We demonstrate the effectiveness of our proposed method in a case study that employs the dataset from the 2016 PHM data challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

AB - Data pre-processing is a crucial step for data management, communication, and modeling in Prognostics and Health Management (PHM) framework. However, most existing studies have primarily focused on feature selection while disregarding the significance of sample selection. In practice, effective sample selection not only decreases data redundancy but also enhances computational efficiency and model performance. This research introduces a new offline sample selection method that employs an optimization algorithm based on simultaneous sparse recovery. We formulate the sample selection problem as a linear programming model that is solvable using most standard convex solvers. The solution of the model not only identifies the important samples but also establishes the mapping relationships between each sample and the important ones. The method can identify a small subset of samples that are crucial by considering their usefulness and freshness. We demonstrate the effectiveness of our proposed method in a case study that employs the dataset from the 2016 PHM data challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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KW - Optimization

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KW - Virtual metrology

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BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023)

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PB - Springer

CY - Cham

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ER -

Zhu F, Feng J, Su Z, Xie M. An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness. In Ball AD, Ouyang H, Sinha JK, Wang Z, editors, Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023). Vol. 1. Cham: Springer . 2024. p. 1119-1129. (Mechanisms and Machine Science). Epub 2024 May 30. doi: 10.1007/978-3-031-49413-0_86

An Optimization-Based Sample Selection Method Considering Sample Redundancy and Usefulness

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