Feature prediction for minority class data augmentation

A computer-usable non-transitory storage medium comprising an augmented plurality of minority-class training records for machine learning, comprising:

• a plurality of original minority-class training records wherein each of the plurality of original minority-class training records is labeled with a same first label and comprises a feature value for each of a plurality of features;
• and
• one or more synthetic minority-class training records having the same first label, wherein each of the one or more synthetic minority-class training records has been generated using a computational method implemented by one or more processors executing software instructions, the computational method comprising:
• changing the feature value of a first feature in an original minority-class training record from a first feature value to a different second feature value, wherein the probability of the first feature having the different second feature value in the original minority-class training record exceeds a pre-determined probability threshold.

A computer-implemented method of training a machine learning model to predict a low-incidence event, the method performed by a computer system, said computer system comprising one or more processors and computer-usable non-transitory storage media operationally coupled to the one or more processors, comprising:

• storing in the non-transitory storage media a plurality of machine-learning training records, the plurality of machine-learning training records comprising:
• a plurality of majority-class training records;
• a plurality of original minority-class training records, each original minority-class training record having a same minority-class label describing a low-incidence event;
• and
• one or more synthetic minority-class training records having the same minority-class label, wherein each of the one or more synthetic minority-class training records has been generated using a computational method, the method comprising:
• changing the feature value of a first feature in an original minority-class training record from a first feature value to a different second feature value, wherein the probability of the first feature having the different second feature value in the original minority-class training record exceeds a pre-determined probability threshold;
• and
• using a computational machine-learning method performed by one or more processors executing software instructions stored in the computer-usable non-transitory storage media, training a machine learning model with the plurality of machine-learning training records to predict the low-incidence event.