Pool-Based Active Learning with Proper Topological Regions
Lies Hadjadj Emilie Devijver Remi Molinier Massih-Reza Amini
Machine learning methods usually rely on large sample size to have good
performance, while it is difficult to provide labeled set in many applications.
Pool-based active learning methods are there to detect, among a set of
unlabeled data, the ones that are the most relevant for the training. We
propose in this paper a meta-approach for pool-based active learning strategies
in the context of multi-class classification tasks based on Proper Topological
Regions. PTR, based on topological data analysis (TDA), are relevant regions
used to sample cold-start points or within the active learning scheme. The
proposed method is illustrated empirically on various benchmark datasets, being
competitive to the classical methods from the literature.