Augmenting data sets for selecting machine learning models
AssigneeOracle International Corporation
InventorsAriel Gedaliah Kobren, Swetasudha Panda, Michael Louis Wick, Qinlan Shen, Jason Anthony Peck
Life of this patent
Estimating the term…
Abstract
Techniques are disclosed for augmenting data sets used for training machine learning models and for generating predictions by trained machine learning models. The techniques generate synthesized data from sample data and train a machine learning model using the synthesized data to augment a sample data set. Embodiments selectively partition the sample data set and synthesized data into a training data and a validation data, which are used to generate and select machine learning models.
Claims20
One or more non-transitory computer-readable media storing instructions that, when executed by one or more hardware processors, cause performance of operations comprising:
- obtaining a plurality of sample data points;
- computing, by a data generator, a plurality of synthetic data points based the plurality of sample data points;
- determining one or more characteristics corresponding to the sample data points;
- selecting a first operation to partition the plurality of sample data points into machine learning training data and machine learning validation data;
- selecting a second operation to partition the plurality of synthetic data points into the machine learning training data and the machine learning validation data;
- wherein at least one of the first operation or the second operation is selected based on the one or more characteristics corresponding to the plurality of sample data points;
- partitioning, using the first operation, the plurality of sample data points into the machine learning training data and the machine learning validation data;
- partitioning, using the second operation, the plurality of synthetic data points into the machine learning training data and the machine learning validation data;
- training a machine learning model using the machine learning training data;
- and
- validating the machine learning model using the machine learning validation data.
One or more non-transitory computer-readable media storing instructions, which when executed by one or more hardware processors, cause performance of operations comprising:
- obtaining a plurality of sample data points;
- determining, by a data set generation module, a plurality of synthetic data points using the plurality of sample data points;
- training a machine learning model using points solely using the plurality of sample data points;
- and
- validating the machine learning model solely using the plurality of synthetic data points.
A method comprising:
- obtaining a plurality of sample data points;
- computing, by a data generator, a plurality of synthetic data points based the plurality of sample data points;
- determining one or more characteristics corresponding to the sample data points;
- selecting a first operation to partition the plurality of sample data points into machine learning training data and machine learning validation data;
- selecting a second operation to partition the plurality of synthetic data points into the machine learning training data and the machine learning validation data;
- wherein at least one of the first operation or the second operation is selected based on the one or more characteristics corresponding to the plurality of sample data points;
- partitioning, using the first operation, the plurality of sample data points into the machine learning training data and the machine learning validation data;
- partitioning, using the second operation, the plurality of synthetic data points into the machine learning training data and the machine learning validation data;
- training a machine learning model using the machine learning training data;
- and
- validating the machine learning model using the machine learning validation data.
A system comprising:
- at least one device including a hardware processor;
- the system being configured to perform operations comprising:
- obtaining a plurality of sample data points;
- computing, by a data generator, a plurality of synthetic data points based the plurality of sample data points;
- determining one or more characteristics corresponding to the sample data points;
- selecting a first operation to partition the plurality of sample data points into machine learning training data and machine learning validation data;
- selecting a second operation to partition the plurality of synthetic data points into the machine learning training data and the machine learning validation data;
- wherein at least one of the first operation or the second operation is selected based on the one or more characteristics corresponding to the plurality of sample data points;
- partitioning, using the first operation, the plurality of sample data points into the machine learning training data and the machine learning validation data;
- partitioning, using the second operation, the plurality of synthetic data points into the machine learning training data and the machine learning validation data;
- training a machine learning model using the machine learning training data;
- and
- validating the machine learning model using the machine learning validation data.
Dependencies are inferred from the claim text.