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A new data cleaning procedure for electron cyclotron emission imaging (ECEI)of EAST tokamak is constructed. Machine learning techniques, including SVM andDecision tree, are applied to identifying saturated, zero, and weak signals ofECEI raw data, which not only reduces the effort of researchers for dataanalysis, but also improves the accuracy of data preprocessing. To enhance thereliability of the procedure, proper training sets are sampled based on massiveraw data from the experiments of ECEI on EAST tokamak. Window size of temporalsignal, kernel function, and other model parameters are obtained after modeltraining. Consequently, the recognition rates of saturated, zero, and weaksignals in raw data are 99.4%, 99.86%, and 99.9%, respectively, which provesthe accuracy of this procedure.

An Accurate Data Cleaning Procedure for Electron Cyclotron Emission Imaging on EAST Tokamak Based on Methodology of Machine Learning