| CPC G16H 50/70 (2018.01) [G06F 17/18 (2013.01); G16B 40/00 (2019.02); G16H 10/60 (2018.01); G16H 50/20 (2018.01)] | 20 Claims | 

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               1. A computer-implemented method of generating synthetic minority-class training records for machine learning, 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 original minority-class training records, including a first minority-class training record, 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, including a first feature, and wherein the first minority-class training record comprises a first feature value for the first feature; 
                using a computational process performed by the one or more processors executing software instructions stored in the computer-usable non-transitory storage media, determining that the probability of the first feature having a different second feature value in the first minority-class training record exceeds a pre-determined probability threshold; and 
                generating a first synthetic minority-class training record from the first minority-class training record, comprising changing the feature value of the first feature in the first minority-class training record from the first feature value to the second feature value, and storing the modified version of the first minority-class training record as the first synthetic minority-class training record in the non-transitory storage media, thereby augmenting the plurality of original minority-class training records with the first synthetic minority-class training record. 
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