Uncertainty sampling is a common method in pool-based active learning to identify the best example to improve a classifier.
for a given predicate p, we use this to choose the best label query involving that predicate, picking that object o ∈ O A which is closest to the hyperplane of the classifier for p. it is more challenging to narrow down the number of predicates.