Adaboost classification of TJ-II Thomson Scattering images

GONZALO ALBERTO FARIAS CASTRO, Sebastián Dormido-Canto, Jesús Vega, Ismael Martínez, Luis Alfaro, Francisco Martínez

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Experiments in thermonuclear fusion generate thousands of signals. Machine learning techniques have shown to be very suitable for implementing pattern recognition systems to fusion databases. The huge amount of data involves performing the analysis in high-dimensional spaces. This makes difficult the searching of patterns with similar properties, which normally produces overfitting. During last years, the use of boosting algorithms has become very popular for avoiding overfitting and building generalized data-driven models. Boosting allows achieving a highly accurate, robust and fast classification by combining many relatively simple rules. In this work, we propose the use of Adaboost algorithm to classify Thomson Scattering images of the TJ-II fusion device. The results are compared with previous works on similar databases.

Original languageEnglish
Pages (from-to)759-763
Number of pages5
JournalFusion Engineering and Design
Volume123
DOIs
StatePublished - 1 Nov 2017

Keywords

  • Adaboost
  • Image classifier
  • Thomson Scattering diagnostic
  • TJ-II

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