About: Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
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 to improve 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. 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 to improve 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. (en)
Title	Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies (en)
skos:prefLabel	Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies (en)
skos:notation	RIV/61389021:_____/13:00421353!RIV14-AV0-61389021
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(GAP205/10/2055)
http://linked.open...iv/cisloPeriodika	7
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Mlynář, Jan Odstrčil, Michal
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav fyziky plazmatu AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	66135
http://linked.open...ai/riv/idVysledku	RIV/61389021:_____/13:00421353
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Learning Machines; Support Vector Machines; Neural Network; ASDEX Upgrade; JET; Disruption mitigation; Tokamaks; ITER (en)
http://linked.open.../riv/klicoveSlovo	ASDEX Upgrade Support Vector Machines JET Neural Network Disruption mitigation Learning Machines ITER Tokamaks
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[AC43149302C2]
http://linked.open...i/riv/nazevZdroje	IEEE Transactions on Plasma Science
http://linked.open...in/vavai/riv/obor	BL
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	Numerical analyses and physical interpretation of the ITER-relevant experimental data from the Joint European Torus JET
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	41
http://linked.open...iv/tvurceVysledku	Mlynář, Jan Murari, A. Odstrčil, Michal
http://linked.open...ain/vavai/riv/wos	000321625400009
issn	0093-3813
number of pages	9 (xsd:int)
http://bibframe.org/vocab/doi	10.1109/TPS.2013.2264880

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