Learning of classification models in practice often relies on a nontrivial amount of human annotation effort. The most widely adopted human labeling process assigns class labels to individual data instances. However, such a process is very rigid and may end up being very time-consuming and costly to conduct in practice. Finding more effective ways to reduce human annotation effort has become critical for building machine learning systems that require human feedback.
In this thesis, we propose and investigate a new machine learning approach - Group-Based Active Learning - to learn classification models from limited human feedback. A group is defined by a set of instances represented by conjunctive patterns that are value ranges over the input features. Such conjunctive patterns define hypercubic regions of the input data space. A human annotator assesses the group solely based on its region-based description by providing an estimate of the class proportion for the subpopulation covered by the region. The advantage of this labeling process is that it allows a human to label many instances at the same time, which can, in turn, improve the labeling efficiency.
In general, there are infinitely many regions one can define over a real-valued input space. To identify and label groups/regions important for classification learning, we propose and develop a Hierarchical Active Learning framework that actively builds and labels a hierarchy of input regions. Briefly, our framework starts by identifying general regions covering substantial portions of the input data space. After that, it progressively splits the regions into smaller and smaller sub-regions and also acquires class proportion labels for the new regions. The proportion labels for these regions are used to gradually improve and refine a classification model induced by the regions. We develop three versions of the idea. The first two versions aim to build a single hierarchy of regions. One builds it statically using hierarchical clustering, while the other one builds it dynamically, similarly to the decision tree learning process. The third approach builds multiple hierarchies simultaneously, and it offers additional flexibility for identifying more informative and simpler regions. We have conducted comprehensive empirical studies to evaluate our framework. The results show that the methods based on the region-based active learning can learn very good classifiers from a very few and simple region queries, and hence are promising for reducing human annotation effort needed for building a variety of classification models
