Title: Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method,
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.