Actively constructing an effective training set by expected gain maximization criterion

We address the problem of active learning which aims to select the most representative points. Out of many existing active learning techniques, close-to-boundary criterion has received considerable attention recently. The goal of typically uncertain-based sampling is to minimize the distance between the examples and the classification boundary. However, these methods only adapt to the independent identically distributed (i.i.d) data, while the non-i.i.d is ignored. In this paper, we propose an active scheme which takes into account of the situation that unlabeled data and training data may come from different distribution, and we approximately obtain the information of every example by expected gain maximization. Given the parameters of a classification model and a pool of unlabeled data, the real risk on the unknown distribution is estimated. The expected gain of every candidate example is obtained, and then the most representative examples of all are thus defined as those who can maximize the expected gain of the classification model. At last, a sampling sequence is acquired by solving the optimization problem. Moreover, we also give an active scheme of two-stage sampling strategy based on the criterion of expected gain maximization in order to obtain an effective training set. The experimental results on MIRFLICKR and Caltech-256 datasets have demonstrated the effectiveness of our proposed methods.