The Synthetic Minority Over-sampling Technique (SMOTE) generates new synthetic training data for a minority class by taking linear combinations of existing data points with their nearest neighbours. This does not necessarily make the the training data more noisy, the quality of the new synthesised data points will depend on the structure of the training data.
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