Data augmentation simulator

A computer system comprising a processor and memory, wherein the processor and the memory in combination are operable to implement a method comprising:

• receiving input data;
• collecting data attributes from the input data based, at least in part, on a type of the input data;
• classifying the input data using the data attributes, the classifying comprising partitioning the input data into a plurality of strata that each represent a subpopulation of the input data;
• performing class-imbalance removal on the classified input data by executing at least a first stage and a second stage of stratified sampling, the performing yielding a balanced dataset, the performing comprising:
• at the first stage of stratified sampling, executing proportionate stratified sampling of the classified input data;
• responsive to the executing, determining, for each stratum of the plurality of strata, whether a sample size of the stratum exceeds a defined threshold;
• and
• responsive to a determination that the sample size of at least one stratum of the plurality of strata exceeds the defined threshold, at the second stage of stratified sampling, executing disproportionate stratified sampling of the classified input data;
• creating integrated data by cyclically performing data processing, modeling, and integration on the balanced dataset, the integrated data comprising the balanced dataset integrated with data-attribute intelligence comprising at least one recommended data attribute of the balanced dataset for extrapolation;
• representing at least a portion of the integrated data as structured knowledge for execution of a particular task;
• applying a plurality of extrapolation algorithms to the structured knowledge to yield raw augmented data, wherein the applying is at least partly based on the at least one recommended data attribute for extrapolation;
• generating an extrapolated dataset using the raw augmented data;
• validating the extrapolated dataset by verifying the extrapolated dataset against stored patterns that ensure acceptable data proportions;
• responsive to a determination that the extrapolated dataset fails to satisfy the stored patterns, reapplying the plurality of extrapolation algorithms to the structured knowledge;
• constructing a synthetic dataset based on the extrapolated dataset, wherein the constructing is performed responsive to a determination that the extrapolated dataset satisfies the stored patterns;
• and
• using the synthetic dataset for artificial intelligence-based data processing in relation to the particular task.

A computer-program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed to implement a method comprising:

• receiving input data;
• collecting data attributes from the input data based, at least in part, on a type of the input data;
• classifying the input data using the data attributes, the classifying comprising partitioning the input data into a plurality of strata that each represent a subpopulation of the input data;
• performing class-imbalance removal on the classified input data by executing at least a first stage and a second stage of stratified sampling, the performing yielding a balanced dataset, the performing comprising:
• at the first stage of stratified sampling, executing proportionate stratified sampling of the classified input data;
• responsive to the executing, determining, for each stratum of the plurality of strata, whether a sample size of the stratum exceeds a defined threshold;
• and
• responsive to a determination that the sample size of at least one stratum of the plurality of strata exceeds the defined threshold, at the second stage of stratified sampling, executing disproportionate stratified sampling of the classified input data;
• creating integrated data by cyclically performing data processing, modeling, and integration on the balanced dataset, the integrated data comprising the balanced dataset integrated with data-attribute intelligence comprising at least one recommended data attribute of the balanced dataset for extrapolation;
• representing at least a portion of the integrated data as structured knowledge for execution of a particular task;
• applying a plurality of extrapolation algorithms to the structured knowledge to yield raw augmented data, wherein the applying is at least partly based on the at least one recommended data attribute for extrapolation;
• generating an extrapolated dataset using the raw augmented data;
• validating the extrapolated dataset by verifying the extrapolated dataset against stored patterns that ensure acceptable data proportions;
• responsive to a determination that the extrapolated dataset fails to satisfy the stored patterns, reapplying the plurality of extrapolation algorithms to the structured knowledge;
• constructing a synthetic dataset based, at least in part, on the extrapolated dataset;
• and
• using the synthetic dataset for artificial intelligence-based data processing in relation to the particular task.