Patent ID: 11941502
Assignee: OPTUM SERVICES (IRELAND) LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 22:
23. The computer-implemented method of claim 22, further comprising generating the first machine learning model and the plurality of subsequent machine learning models by:
receiving the true data set comprising a plurality of true data records organized according to a plurality of true data set columns and a plurality of true data set rows, each true data record of the plurality of true data records comprising a feature vector comprising (i) a plurality of predictor variables and (ii) a plurality of corresponding predictor variable values, wherein (a) each predictor variable represents a unique true data set column of the plurality of true data set columns and (b) each feature vector represents a unique true data set row of the plurality of true data set rows;
generating, based at least in part on the true data set, the adversarial data set comprising a plurality of adversarial data records organized according to a plurality of adversarial data set columns and a plurality of adversarial data set rows, wherein each predictor variable represents an adversarial data set column of the plurality of adversarial data set columns, and wherein the plurality of adversarial data set rows is generated by, for each predictor variable, randomly shuffling the corresponding predictor variable values;
adding a first data set target column to the true data set, wherein each corresponding first data set target row comprises a first classification value;
adding a second data set target column to the adversarial data set, wherein each corresponding second data set target row comprises a second classification value;
generating the first machine learning model configured to distinguish the true data set from the adversarial data set, wherein a first machine learning model output represents a probability or a point belonging to a sub-space represented by the true data set;
removing a true data set column from the true data set and a corresponding adversarial data set column from the adversarial data set that are both associated with a predictor variable of the first machine learning model identified as having a highest feature importance for distinguishing the true data set from the adversarial data set; and
until determining, based at least in part on a subsequent machine learning model, that the true data set can no longer be distinguished from the adversarial data set,
iteratively generating a plurality of subsequent machine learning models each configured to distinguish the true data set from the adversarial data set, wherein the first machine learning model and the plurality of subsequent machine learning models are ranked in an order from most predictive variables to least predictive variables.