Multitask Feature Selection for Objective Reduction

Genghui Li; Qingfu Zhang

doi:10.1007/978-3-030-72062-9_7

Multitask Feature Selection for Objective Reduction

Genghui Li^*
, Qingfu Zhang

^*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

Objective reduction has been regarded as a major tool for solving many-objective optimization problems (MaOPs). This paper proposes a multitask feature selection method for objective reduction. In our proposed method, each objective is formulated as a positive linear combination of a small number of essential objectives, and sparse regularization is employed to identify redundant objectives. Our numerical experiment shows the effectiveness and robustness of the proposed method by comparing it with some state-of-the-art objective reduction methods.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization
Subtitle of host publication	11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings
Editors	Hisao Ishibuchi, Qingfu Zhang, Ran Cheng, Ke Li, Hui Li, Handing Wang, Aimin Zhou
Place of Publication	Cham
Publisher	Springer
Pages	77-88
ISBN (Electronic)	9783030720629
ISBN (Print)	9783030720612
DOIs	https://doi.org/10.1007/978-3-030-72062-9_7
Publication status	Published - 2021
Event	11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021) - Hampton by Hilton Hotel (on-site & on-line), Shenzhen, China Duration: 28 Mar 2021 → 31 Mar 2021

Publication series

Name	Lecture Notes in Computer Science
Volume	12654
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021)
Place	China
City	Shenzhen
Period	28/03/21 → 31/03/21

Research Keywords

Many-objective optimization
Multitask feature selection
Objective reduction

RGC Funding Information

RGC-funded

Access Output

10.1007/978-3-030-72062-9_7

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Li, G., & Zhang, Q. (2021). Multitask Feature Selection for Objective Reduction. In H. Ishibuchi, Q. Zhang, R. Cheng, K. Li, H. Li, H. Wang, & A. Zhou (Eds.), Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings (pp. 77-88). (Lecture Notes in Computer Science; Vol. 12654). Springer . https://doi.org/10.1007/978-3-030-72062-9_7

Li, Genghui ; Zhang, Qingfu. / Multitask Feature Selection for Objective Reduction. Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. editor / Hisao Ishibuchi ; Qingfu Zhang ; Ran Cheng ; Ke Li ; Hui Li ; Handing Wang ; Aimin Zhou. Cham : Springer , 2021. pp. 77-88 (Lecture Notes in Computer Science).

@inproceedings{4d8598a54a044dcb92ae226f379a1e84,

title = "Multitask Feature Selection for Objective Reduction",

abstract = "Objective reduction has been regarded as a major tool for solving many-objective optimization problems (MaOPs). This paper proposes a multitask feature selection method for objective reduction. In our proposed method, each objective is formulated as a positive linear combination of a small number of essential objectives, and sparse regularization is employed to identify redundant objectives. Our numerical experiment shows the effectiveness and robustness of the proposed method by comparing it with some state-of-the-art objective reduction methods.",

keywords = "Many-objective optimization, Multitask feature selection, Objective reduction",

author = "Genghui Li and Qingfu Zhang",

year = "2021",

doi = "10.1007/978-3-030-72062-9\_7",

language = "English",

isbn = "9783030720612",

series = "Lecture Notes in Computer Science",

publisher = "Springer ",

pages = "77--88",

editor = "Hisao Ishibuchi and Qingfu Zhang and Ran Cheng and Ke Li and Hui Li and Handing Wang and Aimin Zhou",

booktitle = "Evolutionary Multi-Criterion Optimization",

note = "11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021) ; Conference date: 28-03-2021 Through 31-03-2021",

}

Li, G & Zhang, Q 2021, Multitask Feature Selection for Objective Reduction. in H Ishibuchi, Q Zhang, R Cheng, K Li, H Li, H Wang & A Zhou (eds), Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. Lecture Notes in Computer Science, vol. 12654, Springer , Cham, pp. 77-88, 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), Shenzhen, China, 28/03/21. https://doi.org/10.1007/978-3-030-72062-9_7

Multitask Feature Selection for Objective Reduction. / Li, Genghui ; Zhang, Qingfu.
Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. ed. / Hisao Ishibuchi; Qingfu Zhang; Ran Cheng; Ke Li; Hui Li; Handing Wang; Aimin Zhou. Cham: Springer , 2021. p. 77-88 (Lecture Notes in Computer Science; Vol. 12654).

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

TY - GEN

T1 - Multitask Feature Selection for Objective Reduction

AU - Li, Genghui

AU - Zhang, Qingfu

PY - 2021

Y1 - 2021

N2 - Objective reduction has been regarded as a major tool for solving many-objective optimization problems (MaOPs). This paper proposes a multitask feature selection method for objective reduction. In our proposed method, each objective is formulated as a positive linear combination of a small number of essential objectives, and sparse regularization is employed to identify redundant objectives. Our numerical experiment shows the effectiveness and robustness of the proposed method by comparing it with some state-of-the-art objective reduction methods.

AB - Objective reduction has been regarded as a major tool for solving many-objective optimization problems (MaOPs). This paper proposes a multitask feature selection method for objective reduction. In our proposed method, each objective is formulated as a positive linear combination of a small number of essential objectives, and sparse regularization is employed to identify redundant objectives. Our numerical experiment shows the effectiveness and robustness of the proposed method by comparing it with some state-of-the-art objective reduction methods.

KW - Many-objective optimization

KW - Multitask feature selection

KW - Objective reduction

UR - https://www.scopus.com/pages/publications/85107294090

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85107294090&origin=recordpage

U2 - 10.1007/978-3-030-72062-9_7

DO - 10.1007/978-3-030-72062-9_7

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783030720612

T3 - Lecture Notes in Computer Science

SP - 77

EP - 88

BT - Evolutionary Multi-Criterion Optimization

A2 - Ishibuchi, Hisao

A2 - Zhang, Qingfu

A2 - Cheng, Ran

A2 - Li, Ke

A2 - Li, Hui

A2 - Wang, Handing

A2 - Zhou, Aimin

PB - Springer

CY - Cham

T2 - 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021)

Y2 - 28 March 2021 through 31 March 2021

ER -

Li G , Zhang Q. Multitask Feature Selection for Objective Reduction. In Ishibuchi H, Zhang Q, Cheng R, Li K, Li H, Wang H, Zhou A, editors, Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings. Cham: Springer . 2021. p. 77-88. (Lecture Notes in Computer Science). Epub 2021 Mar 24. doi: 10.1007/978-3-030-72062-9_7

Multitask Feature Selection for Objective Reduction

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Research Keywords

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