Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method. (16th October 2017)

• Record Type: Journal Article
• Authors: Liu, J.; Lan, T.; Qin, H.
• Is Part Of: Journal of instrumentation. Volume 12:Number 10(2017:Oct.)
• Page Start: C10004
• Page End: C10004
• Publication Date: 2017-10-16
• Subjects: Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/12/10/C10004
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class-imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices. When using machine learning algorithms to classify diagnostic data based on class-imbalanced training set, most classifiers are biased towards the major class and show very poor classification rates on the minor class. By transforming the direct classification problem about original data sequences into a classification problem about the physical similarity between data sequences, the class-balanced effect of Time-Domain Global Similarity (TDGS) method on training set structure is investigated in this paper. Meanwhile, the impact of improved training set structure on data cleaning performance of TDGS method is demonstrated with an application example in EAST POlarimetry-INTerferometry (POINT) system.