Interactive Feature Extraction using Implicit Knowledge Elicitation : Application to Power System Expertise

dc.contributor.author Crochepierre, Laure
dc.contributor.author Boudjeloud-Assala, Lydia
dc.contributor.author Barbesant, Vincent
dc.date.accessioned 2021-12-24T17:32:27Z
dc.date.available 2021-12-24T17:32:27Z
dc.date.issued 2022-01-04
dc.description.abstract Industrial systems such as power networks are continuously monitored by human experts who quickly identify potentially dangerous situations by their experience. As current energy trends increase the complexity of day-to-day grid operations, it becomes necessary to assist experts in their monitoring tasks. This paper proposes an interactive approach to create human-readable analytical expressions that describe physical phenomena by their most impacting quantities. We present an interactive platform that brings experts in the training loop to guide the expression search using their expertise. It uses an evolutionary approach based on Probabilistic Grammar Guided Genetic Programming with expertly created and updated grammars. Interactivity is multi-level: users can distill their knowledge both within and between evolutionary runs. We proposed two usage scenarios on a real-world dataset where the non-interactive algorithm either provides (case 1) or not (case 2) satisfactory solutions. We show improvements regarding the solution's precision (case 1) and complexity (case 2).
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.212
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/79544
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Interactive Visual Analytics and Visualization for Decision Making
dc.subject feature extraction
dc.subject interactivity
dc.subject knowledge elicitation
dc.subject power system
dc.subject visualization
dc.title Interactive Feature Extraction using Implicit Knowledge Elicitation : Application to Power System Expertise
dc.type.dcmi text
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
0169.pdf
Size:
1.01 MB
Format:
Adobe Portable Document Format
Description: