Learning classification models from real-world data often requires substantial human effort devoted to instance annotation. As the instance-based annotating process can be very time-consuming and costly, we propose a novel active learning framework that builds classification models from human-annotatedregions. A region is defined by a set of conjunctive patterns that are formed by value ranges over the input features. A region label is a human assessment of the classproportionin the data population covered by the region. By leveraginglearning from label proportionsalgorithms, regions and their class proportions can be used to train instance-based classification models. However, the key challenge is that in practice, very few regions are defined already. Therefore, to identify regions important for model learning, we design ahierarchical active learning(HAL) framework, which actively builds a hierarchy of regions. Similar to the decision-tree learning process, our approach progressively divides the input data space into smaller sub-regions, solicits labels for the new regions, and retrains the base classification model with all the leaf regions. And we further develop amulti-hierarchy(forest) solution, which builds multiple shallower hierarchies that have more informative, diverse, and simpler regions. We evaluate our HAL framework on numerous impactful classification datasets as well as on a real user study - on the survival analysis of colorectal cancer patients. The results demonstrate that region-based active learning methods can learn high-quality classifiers from very few labeled regions. Hence, our framework is shown very effective in reducing the human annotation effort needed for building classification models.

Learning classification models in practice usually requires numerous labeled data for training. However, instance-based annotation can be inefficient for humans to perform. In this article, we propose and study a new type of human supervision that is fast to perform and useful for model learning. Instead of labeling individual instances, humans provide supervision to dataregions, which are subspaces of the input data space, representing subpopulations of data. Since labeling now is performed on a region level, 0/1 labeling becomes imprecise. Thus, we design the region label to be aqualitativeassessment of the class proportion, which coarsely preserves the labeling precision but is also easy for humans to do. To identify informative regions for labeling and learning, we further devise ahierarchical active learningprocess that recursively constructs a region hierarchy. This process is semisupervised in the sense that it is driven by both active learning strategies and human expertise, where humans can provide discriminative features. To evaluate our framework, we conducted extensive experiments on nine datasets as well as a real user study on a survival analysis of colorectal cancer patients. The results have clearly demonstrated the superiority of our region-based active learning framework against many instance-based active learning methods.