  Majorly classical Active Learning (AL) approach usually uses statistical
theory such as entropy and margin to measure instance utility, however it fails
to capture the data distribution information contained in the unlabeled data.
This can eventually cause the classifier to select outlier instances to label.
Meanwhile, the loss associated with mislabeling an instance in a typical
classification task is much higher than the loss associated with the opposite
error. To address these challenges, we propose a Cost-Based Bugdet Active
Learning (CBAL) which considers the classification uncertainty as well as
instance diversity in a population constrained by a budget. A principled
approach based on the min-max is considered to minimize both the labeling and
decision cost of the selected instances, this ensures a near-optimal results
with significantly less computational effort. Extensive experimental results
show that the proposed approach outperforms several state-of -the-art active
learning approaches.
