Semi-supervised learning using a graph-based phase field model for imbalanced data set classification
Aymen El Ghoul, Hichem Sahbi · 2014
In this paper, we address the problem of semi-supervised learning for binary classification. This task is known to be challenging due to several issues including: the scarceness of labeled data, the large intra-class variability, and also the imbalanced class distributions. Our learning approach is transductive and built upon a graph-based phase field model that handles imbalanced class distributions. This method is able to encourage or penalize the memberships of data to different classes according to an explicit a priori model that avoids biased classifications. Experiments, conducted on real-world benchmarks, show the good performance of our model compared to several state of the art semi-supervised learning algorithms.