Fast Maximum Entropy Machine for Big Imbalanced Datasets

Driven by the need of a plethora of machine learning applications, several attempts have been made at improving the performance of classifiers applied to imbalanced datasets. In this paper, we present a fast maximum entropy machine(MEM)combined with a synthetic minority over-sampling technique for handling binary classification problems with high imbalance ratios, large numbers of data samples, and medium/large numbers of features. A random Fourier feature representation of kernel functions and