Generating quantitatively assessed synthetic training data
Abstract:
In an approach to generating quantitatively assessed synthetic training data, one or more computer processors identify an initial plurality of clusters in a dataset utilizing a trained classification model and a plurality of associated hyperparameters, wherein the clusters have sufficient density to be represented in a calculated probability distribution. The one or more computer processors generate one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution. The one or more computer processors quantitatively assess the one or more generated synthetic data points.
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