Generative Adversarial Networks and Markov Random Fields for oversampling very small training sets. (January 2021) - Salazar, Addisson; Vergara, Luis; Safont, Gonzalo
Highlights: A new method for oversampling very scarce training sets. Based on Generative Adversarial Networks and Markov Random Field models. Much better performance than SMOTE on simulated and real data experiment. Abstract: In this work, we propose a new method for oversampling the training set of a classifier, in a scenario of extreme scarcity of training data. It is based on two concepts: Generative Adversarial Networks (GAN) and vector Markov Random Field (vMRF). Thus, the generative block of GAN uses the vMRF model to synthesize surrogates by the Graph Fourier Transform. Then, the discriminative block implements a linear discriminant on features measuring clique similarities between the synthesized and the original instances. Both blocks iterate until the linear discriminant cannot discriminate the synthetic from the original instances. We have assessed the new method, called Generative Adversarial Network Synthesis for Oversampling (GANSO), with both simulated and real data in experiments where the classifier is to be trained with just 3 or 5 instances. The applications consisted of classification of stages of neuropsychological tests using electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data and classification of sleep stages using electrocardiographic (ECG) data. We have verified that GANSO can effectively improve the classifier performance, while the benchmark method SMOTE is not appropriate to deal with such a small size of the training set.