Active Learning Based on Two Criteria

In many real-world applications plenty of unlabeled instances are available but the number of labeled instances is limited, since labeling the examples requires human efforts and expertise. Therefore, as one type of the paradigms for addressing the problem of combining labeled and unlabeled data to boost the performance, active learning has attracted much attention. Active learning targets to minimize the human annotation efforts by selecting examples for labeling. To maximize the contribution of the selected examples, in this paper, we propose an active learning approach based on two criteria: informativeness and representativeness. The results of experiments show a better performance of our algorithm compared to the current methods.