CO-AutoML: An Optimizable Automated Machine Learning System

Chunnan Wang; Hongzhi Wang; Bo Xu; Xintong Song; Xiangyu Shi; Yuhao Bao; Bo Zheng

doi:10.1007/978-3-031-00129-1_45

CO-AutoML: An Optimizable Automated Machine Learning System

Chunnan Wang
, Hongzhi Wang^*
, Bo Xu
, Xintong Song
, Xiangyu Shi
, Yuhao Bao
, Bo Zheng

^*Corresponding author for this work

Faculty of Computing

Harbin Institute of Technology
Cnosdb Inc.

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

Abstract

In recent years, many automated machine learning (AutoML) techniques are proposed for the automatic selection or design machine learning models. They bring great convenience to the use of machine learning techniques, but are difficult for users without programming experiences to use, and lack of effective optimization scheme to respond to users’ dissatisfaction with final results. To overcome these defects, we develop CO-AutoML, a user-friendly and optimizable AutoML system. CO-AutoML allows users to interact with the system in a customized mode. Besides, it can continuously optimize the search space of the AutoML technique based on reinforce policy and graph neural network (GNN), and thus provide users with more powerful machine learning schemes. Our system empowers ordinary users to easily and more effectively use AutoML techniques, which has a certain application value and practical significance. Our demonstration video: https://youtu.be/nGnmA7noeJA.

Original language	English
Title of host publication	Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings
Editors	Arnab Bhattacharya, Janice Lee Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh Mohania, Anirban Mondal, Vikram Goyal, Rage Uday Kiran
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	509-513
Number of pages	5
ISBN (Print)	9783031001284
DOIs	https://doi.org/10.1007/978-3-031-00129-1_45
State	Published - 2022
Event	27th International Conference on Database Systems for Advanced Applications, DASFAA 2022 - Virtual, Online Duration: 11 Apr 2022 → 14 Apr 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13247 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Database Systems for Advanced Applications, DASFAA 2022
City	Virtual, Online
Period	11/04/22 → 14/04/22

Keywords

Automated machine learning
Search space optimization

Access to Document

10.1007/978-3-031-00129-1_45

Cite this

Wang, C., Wang, H., Xu, B., Song, X., Shi, X., Bao, Y., & Zheng, B. (2022). CO-AutoML: An Optimizable Automated Machine Learning System. In A. Bhattacharya, J. Lee Mong Li, D. Agrawal, P. K. Reddy, M. Mohania, A. Mondal, V. Goyal, & R. Uday Kiran (Eds.), Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings (pp. 509-513). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13247 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-00129-1_45

Wang, Chunnan ; Wang, Hongzhi ; Xu, Bo et al. / CO-AutoML : An Optimizable Automated Machine Learning System. Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings. editor / Arnab Bhattacharya ; Janice Lee Mong Li ; Divyakant Agrawal ; P. Krishna Reddy ; Mukesh Mohania ; Anirban Mondal ; Vikram Goyal ; Rage Uday Kiran. Springer Science and Business Media Deutschland GmbH, 2022. pp. 509-513 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c4fcecc2ad5d423cb63951712d9c8332,

title = "CO-AutoML: An Optimizable Automated Machine Learning System",

abstract = "In recent years, many automated machine learning (AutoML) techniques are proposed for the automatic selection or design machine learning models. They bring great convenience to the use of machine learning techniques, but are difficult for users without programming experiences to use, and lack of effective optimization scheme to respond to users{\textquoteright} dissatisfaction with final results. To overcome these defects, we develop CO-AutoML, a user-friendly and optimizable AutoML system. CO-AutoML allows users to interact with the system in a customized mode. Besides, it can continuously optimize the search space of the AutoML technique based on reinforce policy and graph neural network (GNN), and thus provide users with more powerful machine learning schemes. Our system empowers ordinary users to easily and more effectively use AutoML techniques, which has a certain application value and practical significance. Our demonstration video: https://youtu.be/nGnmA7noeJA.",

keywords = "Automated machine learning, Search space optimization",

author = "Chunnan Wang and Hongzhi Wang and Bo Xu and Xintong Song and Xiangyu Shi and Yuhao Bao and Bo Zheng",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022 ; Conference date: 11-04-2022 Through 14-04-2022",

year = "2022",

doi = "10.1007/978-3-031-00129-1\_45",

language = "英语",

isbn = "9783031001284",

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

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "509--513",

editor = "Arnab Bhattacharya and \{Lee Mong Li\}, Janice and Divyakant Agrawal and Reddy, \{P. Krishna\} and Mukesh Mohania and Anirban Mondal and Vikram Goyal and \{Uday Kiran\}, Rage",

booktitle = "Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings",

address = "德国",

}

Wang, C, Wang, H, Xu, B, Song, X, Shi, X, Bao, Y & Zheng, B 2022, CO-AutoML: An Optimizable Automated Machine Learning System. in A Bhattacharya, J Lee Mong Li, D Agrawal, PK Reddy, M Mohania, A Mondal, V Goyal & R Uday Kiran (eds), Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13247 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 509-513, 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022, Virtual, Online, 11/04/22. https://doi.org/10.1007/978-3-031-00129-1_45

CO-AutoML: An Optimizable Automated Machine Learning System. / Wang, Chunnan; Wang, Hongzhi; Xu, Bo et al.
Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings. ed. / Arnab Bhattacharya; Janice Lee Mong Li; Divyakant Agrawal; P. Krishna Reddy; Mukesh Mohania; Anirban Mondal; Vikram Goyal; Rage Uday Kiran. Springer Science and Business Media Deutschland GmbH, 2022. p. 509-513 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13247 LNCS).

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

TY - GEN

T1 - CO-AutoML

T2 - 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022

AU - Wang, Chunnan

AU - Wang, Hongzhi

AU - Xu, Bo

AU - Song, Xintong

AU - Shi, Xiangyu

AU - Bao, Yuhao

AU - Zheng, Bo

PY - 2022

Y1 - 2022

N2 - In recent years, many automated machine learning (AutoML) techniques are proposed for the automatic selection or design machine learning models. They bring great convenience to the use of machine learning techniques, but are difficult for users without programming experiences to use, and lack of effective optimization scheme to respond to users’ dissatisfaction with final results. To overcome these defects, we develop CO-AutoML, a user-friendly and optimizable AutoML system. CO-AutoML allows users to interact with the system in a customized mode. Besides, it can continuously optimize the search space of the AutoML technique based on reinforce policy and graph neural network (GNN), and thus provide users with more powerful machine learning schemes. Our system empowers ordinary users to easily and more effectively use AutoML techniques, which has a certain application value and practical significance. Our demonstration video: https://youtu.be/nGnmA7noeJA.

AB - In recent years, many automated machine learning (AutoML) techniques are proposed for the automatic selection or design machine learning models. They bring great convenience to the use of machine learning techniques, but are difficult for users without programming experiences to use, and lack of effective optimization scheme to respond to users’ dissatisfaction with final results. To overcome these defects, we develop CO-AutoML, a user-friendly and optimizable AutoML system. CO-AutoML allows users to interact with the system in a customized mode. Besides, it can continuously optimize the search space of the AutoML technique based on reinforce policy and graph neural network (GNN), and thus provide users with more powerful machine learning schemes. Our system empowers ordinary users to easily and more effectively use AutoML techniques, which has a certain application value and practical significance. Our demonstration video: https://youtu.be/nGnmA7noeJA.

KW - Automated machine learning

KW - Search space optimization

UR - https://www.scopus.com/pages/publications/85128875142

U2 - 10.1007/978-3-031-00129-1_45

DO - 10.1007/978-3-031-00129-1_45

M3 - 会议稿件

AN - SCOPUS:85128875142

SN - 9783031001284

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

SP - 509

EP - 513

BT - Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings

A2 - Bhattacharya, Arnab

A2 - Lee Mong Li, Janice

A2 - Agrawal, Divyakant

A2 - Reddy, P. Krishna

A2 - Mohania, Mukesh

A2 - Mondal, Anirban

A2 - Goyal, Vikram

A2 - Uday Kiran, Rage

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 11 April 2022 through 14 April 2022

ER -

Wang C, Wang H, Xu B, Song X, Shi X, Bao Y et al. CO-AutoML: An Optimizable Automated Machine Learning System. In Bhattacharya A, Lee Mong Li J, Agrawal D, Reddy PK, Mohania M, Mondal A, Goyal V, Uday Kiran R, editors, Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 509-513. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-00129-1_45

CO-AutoML: An Optimizable Automated Machine Learning System

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