Active Sample Selection Through Sparse Neighborhood for Imbalanced Datasets For the imbalanced datasets, a novel biasedbased active sampling learning algorithm is proposed for the first time. The algorithm combines two important sampling factors with minority confidence and instancesxe2x80x99 informativeness in active learning framework The sampling strategy aims at taking into account the selected instancesxe2x80x99 utilities while avoiding sampling invalid majority instances. For this purpose, a novel label propagation algorithm through sparse neighborhood independent of superparameter k is proposed to calculate minority confidence. Different from other semisupervised learning methods, the algorithm learns the instances by sparse coding theory and adaptively constructs the sparse neighborhood and the sparse neighborhood graph. For calculating instancesxe2x80x99 informativeness, we propose an informativeness measure method based on the nearest boundary distance. It mainly utilizes direction vector feature and a heuristic search strategy to construct an auxiliary decision boundary. Then we evaluate the instancesxe2x80x99 informativeness based on the auxiliary decision boundary.

Learning to Sample: An Active Learning Framework Metalearning algorithms for active learning are emerging as a promising paradigm for learning the best active learning strategy. However, current learningbased active learning approaches still require sufficient training data so as to generalize metalearning models for active learning. This is contrary to the nature of active learning which typically starts with a small number of labeled samples. The unavailability of large amounts of labeled samples for training metalearning models would inevitably lead to poor performance (e.g., instabilities and overfitting). In our paper, we tackle these issues by proposing a novel learningbased active learning framework, called Learning To Sample (LTS). This framework has two key components: a sampling model and a boosting model, which can mutually learn from each other in iterations to improve the performance of each other. Within this framework, the sampling model incorporates uncertainty sampling and diversity sampling into a unified process for optimization, enabling us to actively select the most representative and informative samples based on an optimized integration of uncertainty and diversity. To evaluate the effectiveness of the LTS framework, we have conducted extensive experiments on three different classification tasks: image classification, salary level prediction, and entity resolution. The experimental results show that our LTS framework significantly outperforms all the baselines when the label budget is limited, especially for datasets with highly imbalanced classes. In addition to this, our LTS framework can effectively tackle the cold start problem occurring in many existing active learning approaches.