1. A computer-implemented method to determine a number of kernels within a model for balancing training data using machine learning, the computer-implemented method comprising: receiving, by a computer, an input to balance an imbalanced training data set that includes a majority data class and a minority data class; determining, by the computer, the number of kernels that include data samples of the majority data class of the imbalanced training data set based on a set of generated artificial data samples for the minority data class of the imbalanced training data set; labeling each datum in the majority data class and the minority data class with a corresponding label that identifies corresponding datum for clustering by an expectation maximization algorithm; generating, by the computer, the model of an estimated data distribution of recorded negative data samples associated with the majority data class of the imbalanced training data set including the set of generated artificial data samples selectively injected into the minority data class as positive data samples to constrain the model and balance the imbalanced training data set across different data classes of the majority data class and the minority data class of the imbalanced training data set, the model including the number of kernels representing a set of one or more kernels of clustered data within a sample data space; calculating, by the computer using predetermined criteria, a likelihood that the positive data samples are included in negative data samples of the majority data class of the imbalanced training data set, wherein a set of positive data samples that is less likely to be included in the negative data samples of the majority data class represents a data distribution of the minority data class selectively injected by the computer to balance a number of positive data samples in the minority data class with a number of negative data samples in the majority data class to form labeled and balanced training data that train a multi-class classifier model; updating, by the computer, parameters of each kernel in the number of kernels in parallel using the expectation maximization algorithm, based on the set of positive data samples calculated less likely to be included in the negative data samples of the majority data class; adjusting, by the computer, each kernel in the number of kernels based on the parameters updated while maintaining existing known data of the majority data class of the imbalanced training data set; and training, by the computer using the labeled and balanced training data, the multi-class classifier model of a supervised machine learning program of a system.