Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389021%3A_____%2F13%3A00421353" target="_blank" >RIV/61389021:_____/13:00421353 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/TPS.2013.2264880" target="_blank" >http://dx.doi.org/10.1109/TPS.2013.2264880</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TPS.2013.2264880" target="_blank" >10.1109/TPS.2013.2264880</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies
Original language description
Machine learning tools have been used since a long time ago to study disruptions and to predict their occurrence. On the other hand, the challenges posed by the quality and quantities of the data available remain substantial. In this paper, methods to optimize the training data set and the potential of kernels-based advanced machine learning tools are explored and assessed. Various alternatives, ranging from appropriate selection of the weights to the inclusion of artificial points, are investigated toimprove the quality of the training data set. Support vector machines (SVM), relevance vector machines (RVMs), and one-class SVM are compared. The relative performances of the different approaches are initially assessed using synthetic data. Then they are applied to a relatively large database of JET disruptions. It is shown that in terms of final results, the optimization of the training databases proved to be very productive.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BL - Plasma physics and discharge through gases
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/GAP205%2F10%2F2055" target="_blank" >GAP205/10/2055: Numerical analyses and physical interpretation of the ITER-relevant experimental data from the Joint European Torus JET</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Plasma Science
ISSN
0093-3813
e-ISSN
—
Volume of the periodical
41
Issue of the periodical within the volume
7
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
1751-1759
UT code for WoS article
000321625400009
EID of the result in the Scopus database
—