%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} }