1. A computer-implemented method for determining a number of kernels within a model, further comprising: determining, by a computer, a number of kernels that include data samples of a majority data class of an imbalanced training data set based on a set of generated artificial data samples for a minority data class of the imbalanced training data set; generating, by the computer, the number of kernels within the model based on the set of generated artificial data samples; calculating, by the computer, a likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set; updating, by the computer, parameters of each kernel in the number of kernels based on the likelihood of the set of generated artificial data samples being included in the majority data class of the imbalanced training data set; and adjusting, by the computer, each kernel in the number of kernels based on the updated parameters.