Learning partial-value variable relations for system modeling

Nong Ye; Ting Yan Fok; Xin Wang; James Collofello; Nancy Dickson

doi:10.1109/ICCAR.2018.8384702

Learning partial-value variable relations for system modeling

Nong Ye, Ting Yan Fok, Xin Wang, James Collofello, Nancy Dickson

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

3 Scopus citations

Abstract

An important part of system modeling involves establishing relations of system variables. Data collected from a system reflects relations of system variables and thus allows us to learn variable relations from system data. Existing machine learning and data mining techniques focus on learning variable relations that hold for all values of variables. However, different variable relations may exist for different ranges of variable values, or a variable relation holds only for certain ranges of variable values but not for full ranges of variable values. This paper presents the use of a new algorithm, called Partial-Value Association Discovery (PVAD), to learn partial-value variable relations for energy consumption system modeling and engineering retention.

Original language	English (US)
Title of host publication	Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	368-372
Number of pages	5
ISBN (Electronic)	9781538663387
DOIs	https://doi.org/10.1109/ICCAR.2018.8384702
State	Published - Jun 13 2018
Event	4th International Conference on Control, Automation and Robotics, ICCAR 2018 - Auckland, New Zealand Duration: Apr 20 2018 → Apr 23 2018

Other

Other	4th International Conference on Control, Automation and Robotics, ICCAR 2018
Country	New Zealand
City	Auckland
Period	4/20/18 → 4/23/18

Keywords

data mining
machine learning
system modeling

ASJC Scopus subject areas

Artificial Intelligence
Mechanical Engineering
Control and Optimization

Access to Document

10.1109/ICCAR.2018.8384702

Fingerprint Dive into the research topics of 'Learning partial-value variable relations for system modeling'. Together they form a unique fingerprint.

Cite this

Learning partial-value variable relations for system modeling. / Ye, Nong; Fok, Ting Yan; Wang, Xin; Collofello, James; Dickson, Nancy.

Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 368-372 8384702.

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

Ye, N, Fok, TY, Wang, X, Collofello, J & Dickson, N 2018, Learning partial-value variable relations for system modeling. in Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018., 8384702, Institute of Electrical and Electronics Engineers Inc., pp. 368-372, 4th International Conference on Control, Automation and Robotics, ICCAR 2018, Auckland, New Zealand, 4/20/18. https://doi.org/10.1109/ICCAR.2018.8384702

@inproceedings{59d6fc2d8fe843859362234859dc3681,

title = "Learning partial-value variable relations for system modeling",

abstract = "An important part of system modeling involves establishing relations of system variables. Data collected from a system reflects relations of system variables and thus allows us to learn variable relations from system data. Existing machine learning and data mining techniques focus on learning variable relations that hold for all values of variables. However, different variable relations may exist for different ranges of variable values, or a variable relation holds only for certain ranges of variable values but not for full ranges of variable values. This paper presents the use of a new algorithm, called Partial-Value Association Discovery (PVAD), to learn partial-value variable relations for energy consumption system modeling and engineering retention.",

keywords = "data mining, machine learning, system modeling",

author = "Nong Ye and Fok, {Ting Yan} and Xin Wang and James Collofello and Nancy Dickson",

year = "2018",

month = jun,

day = "13",

doi = "10.1109/ICCAR.2018.8384702",

language = "English (US)",

pages = "368--372",

booktitle = "Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "4th International Conference on Control, Automation and Robotics, ICCAR 2018 ; Conference date: 20-04-2018 Through 23-04-2018",

}

TY - GEN

T1 - Learning partial-value variable relations for system modeling

AU - Ye, Nong

AU - Fok, Ting Yan

AU - Wang, Xin

AU - Collofello, James

AU - Dickson, Nancy

PY - 2018/6/13

Y1 - 2018/6/13

N2 - An important part of system modeling involves establishing relations of system variables. Data collected from a system reflects relations of system variables and thus allows us to learn variable relations from system data. Existing machine learning and data mining techniques focus on learning variable relations that hold for all values of variables. However, different variable relations may exist for different ranges of variable values, or a variable relation holds only for certain ranges of variable values but not for full ranges of variable values. This paper presents the use of a new algorithm, called Partial-Value Association Discovery (PVAD), to learn partial-value variable relations for energy consumption system modeling and engineering retention.

AB - An important part of system modeling involves establishing relations of system variables. Data collected from a system reflects relations of system variables and thus allows us to learn variable relations from system data. Existing machine learning and data mining techniques focus on learning variable relations that hold for all values of variables. However, different variable relations may exist for different ranges of variable values, or a variable relation holds only for certain ranges of variable values but not for full ranges of variable values. This paper presents the use of a new algorithm, called Partial-Value Association Discovery (PVAD), to learn partial-value variable relations for energy consumption system modeling and engineering retention.

KW - data mining

KW - machine learning

KW - system modeling

UR - http://www.scopus.com/inward/record.url?scp=85049904996&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049904996&partnerID=8YFLogxK

U2 - 10.1109/ICCAR.2018.8384702

DO - 10.1109/ICCAR.2018.8384702

M3 - Conference contribution

AN - SCOPUS:85049904996

SP - 368

EP - 372

BT - Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference on Control, Automation and Robotics, ICCAR 2018

Y2 - 20 April 2018 through 23 April 2018

ER -