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Multi-target prediction : a unifying view on problems and methods

Willem Waegeman (UGent) , Krzysztof Dembczyński and Eyke Hüllermeier
(2019) DATA MINING AND KNOWLEDGE DISCOVERY. 33(2). p.293-324
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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.
Keywords
Multivariate regression, Multi-label classification, Multi-task learning, Pairwise learning, Dyadic prediction, Zero-shot learning, Collaborative filtering, MULTILABEL CLASSIFICATION, MULTIPLE TASKS, LABEL, REGRESSION, FRAMEWORK, KERNELS

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MLA
Waegeman, Willem, Krzysztof Dembczyński, and Eyke Hüllermeier. “Multi-target Prediction : a Unifying View on Problems and Methods.” DATA MINING AND KNOWLEDGE DISCOVERY 33.2 (2019): 293–324. Print.
APA
Waegeman, W., Dembczyński, K., & Hüllermeier, E. (2019). Multi-target prediction : a unifying view on problems and methods. DATA MINING AND KNOWLEDGE DISCOVERY, 33(2), 293–324.
Chicago author-date
Waegeman, Willem, Krzysztof Dembczyński, and Eyke Hüllermeier. 2019. “Multi-target Prediction : a Unifying View on Problems and Methods.” Data Mining and Knowledge Discovery 33 (2): 293–324.
Chicago author-date (all authors)
Waegeman, Willem, Krzysztof Dembczyński, and Eyke Hüllermeier. 2019. “Multi-target Prediction : a Unifying View on Problems and Methods.” Data Mining and Knowledge Discovery 33 (2): 293–324.
Vancouver
1.
Waegeman W, Dembczyński K, Hüllermeier E. Multi-target prediction : a unifying view on problems and methods. DATA MINING AND KNOWLEDGE DISCOVERY. 2019;33(2):293–324.
IEEE
[1]
W. Waegeman, K. Dembczyński, and E. Hüllermeier, “Multi-target prediction : a unifying view on problems and methods,” DATA MINING AND KNOWLEDGE DISCOVERY, vol. 33, no. 2, pp. 293–324, 2019.
@article{8606324,
  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 Dembczyński, Krzysztof and Hüllermeier, Eyke},
  issn         = {1384-5810},
  journal      = {DATA MINING AND KNOWLEDGE DISCOVERY},
  keywords     = {Multivariate regression,Multi-label classification,Multi-task learning,Pairwise learning,Dyadic prediction,Zero-shot learning,Collaborative filtering,MULTILABEL CLASSIFICATION,MULTIPLE TASKS,LABEL,REGRESSION,FRAMEWORK,KERNELS},
  language     = {eng},
  number       = {2},
  pages        = {293--324},
  title        = {Multi-target prediction : a unifying view on problems and methods},
  url          = {http://dx.doi.org/10.1007/s10618-018-0595-5},
  volume       = {33},
  year         = {2019},
}
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