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Machine learning methods usually rely on large sample size to have goodperformance, while it is difficult to provide labeled set in many applications.Pool-based active learning methods are there to detect, among a set ofunlabeled data, the ones that are the most relevant for the training. Wepropose in this paper a meta-approach for pool-based active learning strategiesin the context of multi-class classification tasks based on Proper TopologicalRegions. PTR, based on topological data analysis (TDA), are relevant regionsused to sample cold-start points or within the active learning scheme. Theproposed method is illustrated empirically on various benchmark datasets, beingcompetitive to the classical methods from the literature.

Pool-Based Active Learning with Proper Topological Regions