Multi-target prediction: a unifying view on problems and methods
| Type of publication: | Article |
| Citation: | waegeman19multitarget |
| Journal: | Data Mining and Knowledge Discovery |
| Volume: | 33 |
| Number: | 2 |
| Year: | 2019 |
| Month: | March |
| 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 |
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