@article{15002,
  abstract     = {{Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (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.}},
  author       = {{Waegeman, Willem and Dembczynski, Krzysztof and Hüllermeier, Eyke}},
  issn         = {{1573-756X}},
  journal      = {{Data Mining and Knowledge Discovery}},
  number       = {{2}},
  pages        = {{293--324}},
  title        = {{{Multi-target prediction: a unifying view on problems and methods}}},
  doi          = {{10.1007/s10618-018-0595-5}},
  volume       = {{33}},
  year         = {{2019}},
}