Generalized Multi-Instance Learning: Problems, Algorithms and Data Sets

In multi-instance learning, each example is represented by a bag of instances while associated with a binary label. Under standard multi-instance learning settings, one example is labeled as a positive bag if at least one of its instances is positive. Otherwise, it is labeled as a negative bag. Although based on the above assumption, standard multi-instance learning has achieved much success in solving diverse learning tasks, there are still many real-world problems where this assumption may not necessarily hold. Therefore, researchers aimed to expand the underlying assumption of standard multi-instance learning where two frameworks of generalized multi-instance learning have been proposed. In this paper, the problem definition, learning algorithms and also experimental data sets related to either generalized multi-instance learning framework are briefly reviewed.