Active learning for binary classification with abstention


We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three abstention settings: fixed-cost and two variants of boundedrate abstention, and for each of them propose an active learning algorithm. All the proposed algorithms can work in the most commonly used active learning models, i.e., membership-query, pool-based, and stream-based sampling. We obtain upper-bounds on the excess risk of our algorithms in a general non-parametric framework, and establish their minimax near-optimality by deriving matching lower-bounds. Since our algorithms rely on the knowledge of some smoothness parameters of the regression function, we then describe a new strategy to adapt to these unknown parameters in a data-driven manner. Since the worst case computational complexity of our proposed algorithms increases exponentially with the dimension of the input space, we conclude the paper with a computationally efficient variant of our algorithm whose computational complexity has a polynomial dependence over a smaller but rich class of learning problems

IEEE International Symposium on Information Theory