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 | |
| 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 |
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Keywords
- data mining
- machine learning
- system modeling
ASJC Scopus subject areas
- Artificial Intelligence
- Mechanical Engineering
- Control and Optimization
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
}
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.
ER -