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• W2563894485 abstract "Vast and increasing volumes of data are generated by the large amount of smart meters in the power grid. To take advantage of information mining, the raw data quality should be enhanced by cleaning and filtering before it reaches analyzer. This paper reports on fault record detection and diagnosis with an optimized random forest method. The model is illustrated on a simple example and the feature selection and the size of training set for imbalanced class are discussed in the paper. Comparison of performance and effectiveness with other classification algorithm implements are reported in the final of the paper." @default.
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• W2563894485 date "2016-10-01" @default.
• W2563894485 modified "2023-09-24" @default.
• W2563894485 title "Fault record detection with random forests in data center of large power grid" @default.
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• W2563894485 doi "https://doi.org/10.1109/appeec.2016.7779771" @default.
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