Multi-Target Prediction: A Unifying View on Problems and Methods
This work addresses the need for a cohesive understanding in the machine learning community by unifying diverse MTP subfields, though it is incremental as it synthesizes existing knowledge rather than proposing new methods.
The paper tackles the lack of a unified framework for multi-target prediction (MTP) problems, which involve predicting multiple target variables, by introducing a general framework that covers subfields like multi-label classification and multi-task learning, and provides a structured overview of methods based on key properties.
Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.