Improved Graph-based semi-supervised learning Schemes
This work addresses classification challenges in data-scarce and imbalanced scenarios, but it is incremental as it modifies existing methods.
The authors tackled the problem of classifying large datasets with few labels by improving graph-based semi-supervised learning algorithms, resulting in increased accuracy and robustness, particularly for imbalanced datasets.
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.