Online ML Self-adaptation in Face of Traps
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10474035" target="_blank" >RIV/00216208:11320/23:10474035 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ACSOS58161.2023.00023" target="_blank" >https://doi.org/10.1109/ACSOS58161.2023.00023</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACSOS58161.2023.00023" target="_blank" >10.1109/ACSOS58161.2023.00023</a>
Alternative languages
Result language
angličtina
Original language name
Online ML Self-adaptation in Face of Traps
Original language description
Online machine learning (ML) is often used in selfadaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report its limitations. Recently, we experimented with applying online ML for self-adaptation of a smart farming scenario and we had faced several unexpected difficulties - traps - that, to our knowledge, are not discussed enough in the community. In this paper, we report our experience with these traps. Specifically, we discuss several traps that relate to the specification and online training of the ML-based estimators, their impact on self-adaptation, and the approach used to evaluate the estimators. Our overview of these traps provides a list of lessons learned, which can serve as guidance for other researchers and practitioners when applying online ML for self-adaptation.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING AND SELF-ORGANIZING SYSTEMS, ACSOS
ISBN
979-8-3503-3744-0
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
57-66
Publisher name
IEEE COMPUTER SOC
Place of publication
LOS ALAMITOS
Event location
Toronto
Event date
Sep 25, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001122711700007