  Supervised machine learning often requires large training sets to train
accurate models, yet obtaining large amounts of labeled data is not always
feasible. Hence, it becomes crucial to explore active learning methods for
reducing the size of training sets while maintaining high accuracy. The aim is
to select the optimal subset of data for labeling from an initial unlabeled
set, ensuring precise prediction of outcomes. However, conventional active
learning approaches are comparable to classical random sampling. This paper
proposes a wrapper active learning method for classification, organizing the
sampling process into a tree structure, that improves state-of-the-art
algorithms. A classification tree constructed on an initial set of labeled
samples is considered to decompose the space into low-entropy regions.
Input-space based criteria are used thereafter to sub-sample from these
regions, the total number of points to be labeled being decomposed into each
region. This adaptation proves to be a significant enhancement over existing
active learning methods. Through experiments conducted on various benchmark
data sets, the paper demonstrates the efficacy of the proposed framework by
being effective in constructing accurate classification models, even when
provided with a severely restricted labeled data set.
