%Aigaion2 BibTeX export from Knowledge Engineering Publications
%Friday 17 December 2021 11:56:32 PM

@ARTICLE{waegeman19multitarget,
    author = {Waegeman, Willem and Dembczy\'nski, Krzysztof and H{\"{u}}llermeier, Eyke},
     month = mar,
     title = {Multi-target prediction: a unifying view on problems and methods},
   journal = {Data Mining and Knowledge Discovery},
    volume = {33},
    number = {2},
      year = {2019},
     pages = {293--324},
      note = {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.},
      issn = {1573-756X},
       url = {https://doi.org/10.1007/s10618-018-0595-5},
       doi = {10.1007/s10618-018-0595-5}
}