Active Learning-Based Optimization of Scientific Experimental Design. (arXiv:2112.14811v1 [cs.LG])

Active learning (AL) is a machine learning algorithm that can achieve greater
accuracy with fewer labeled training instances, for having the ability to ask
oracles to label the most valuable unlabeled data chosen iteratively and
heuristically by query strategies. Scientific experiments nowadays, though
becoming increasingly automated, are still suffering from human involvement in
the designing process and the exhaustive search in the experimental space. This
article performs a retrospective study on a drug response dataset using the
proposed AL scheme …

active learning arxiv design experimental learning optimization