  In this work, we improve the accuracy of several known algorithms to address
the classification of large datasets when few labels are available. Our
framework lies in the realm of graph-based semi-supervised learning. With novel
modifications on Gaussian Random Fields Learning and Poisson Learning
algorithms, we increase the accuracy and create more robust algorithms.
Experimental results demonstrate the efficiency and superiority of the proposed
methods over conventional graph-based semi-supervised techniques, especially in
the context of imbalanced datasets.
