1. A computer-implemented method for injecting generated artificial data samples into a minority data class of an imbalanced training data set, the computer-implemented method comprising: responsive to a computer receiving an input to balance the imbalanced training data set that includes a majority data class and the minority data class, generating, by the computer, a set of artificial data samples for the minority data class of the imbalanced training data set; calculating, by the computer, a distance from each data sample in the set of generated artificial data samples to a center of a kernel that includes a set of data samples of the majority data class; storing, by the computer, each data sample in the set of generated artificial data samples within a corresponding distance score bucket based on the calculated distance of a data sample; selecting, by the computer, generated artificial data samples from a predetermined number of highest ranking distance score buckets; and injecting, by the computer, the generated artificial data samples selected from the predetermined number of highest ranking distance score buckets into the minority data class to balance a size of the minority data class with a size of the majority data class.