Generating quantitatively assessed synthetic training data
AssigneeInternational Business Machines Corporation
InventorsGabriele Ranco, Moises Noe Sanchez Garcia, Gordon Doyle
Life of this patent
Estimating the term…
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.
Claims20
A computer-implemented method comprising:
- identifying, by one or more computer processors, 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;
- generating, by one or more computer processors, one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution;
- and
- quantitatively assessing, by one or more computer processors, the one or more generated synthetic data points.
A computer program product comprising:
- one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the stored program instructions comprising:
- program instructions to 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;
- program instructions to generate one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution;
- and
- program instructions to quantitatively assess the one or more generated synthetic data points.
A computer system comprising:
- one or more computer processors;
- one or more computer readable storage media;
- and
- program instructions stored on the computer readable storage media for execution by at least one of the one or more processors, the stored program instructions comprising:
- program instructions to 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;
- program instructions to generate one or more synthetic data points for each identified cluster utilizing a corresponding calculated probability distribution;
- and
- program instructions to quantitatively assess the one or more generated synthetic data points.
Dependencies are inferred from the claim text.