Online active classification via margin-based and feature-based label queries

摘要

In the paradigm of online active classification, the learner not only has to predict the label of each incoming instance, but also must decide whether the true label of that instance should be supplied, or not. The overall goal is to minimize the number of prediction mistakes with few label queries. In this paper, we focus on a novel framework for online active learning, with the aim of handling high dimensional classification problems. The key component of our framework is to exploit both the margin-based predictive uncertainty and the feature-based discriminative information of the current instance, in order to determine whether it should be labeled. Based on this labeling strategy, we propose several online active learning algorithms, for both binary classification tasks and multiclass ones. For these algorithms, which use adaptive subgradient methods for updating their linear model, expected mistake bounds are provided. Experiments on high-dimensional (binary and multiclass) classification datasets reveal the benefit of our label query strategy, and show the superiority of our algorithms over the existing ones.