Pool-Based Active Learning with Proper Topological Regions
- URL: http://arxiv.org/abs/2310.01597v1
- Date: Mon, 2 Oct 2023 19:42:33 GMT
- Title: Pool-Based Active Learning with Proper Topological Regions
- Authors: Lies Hadjadj, Emilie Devijver, Remi Molinier, Massih-Reza Amini
- Abstract summary: 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.
- Score: 5.5165579223151795
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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