  Labeling each instance in a large dataset is extremely labor- and time-
consuming . One way to alleviate this problem is active learning, which aims to
which discover the most valuable instances for labeling to construct a powerful
classifier. Considering both informativeness and representativeness provides a
promising way to design a practical active learning. However, most existing
active learning methods select instances favoring either informativeness or
representativeness. Meanwhile, many are designed based on the binary class, so
that they may present suboptimal solutions on the datasets with multiple
classes. In this paper, a hybrid informative and representative criterion based
multi-class active learning approach is proposed. We combine the informative
informativeness and representativeness into one formula, which can be solved
under a unified framework. The informativeness is measured by the margin
minimum while the representative information is measured by the maximum mean
discrepancy. By minimizing the upper bound for the true risk, we generalize the
empirical risk minimization principle to the active learning setting.
Simultaneously, our proposed method makes full use of the label information,
and the proposed active learning is designed based on multiple classes. So the
proposed method is not suitable to the binary class but also the multiple
classes. We conduct our experiments on twelve benchmark UCI data sets, and the
experimental results demonstrate that the proposed method performs better than
some state-of-the-art methods.
