
Multi-target prediction : a unifying view on problems and methods
- Author
- Willem Waegeman (UGent) , Krzysztof Dembczyński and Eyke Hüllermeier
- Organization
- 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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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8606324
- MLA
- Waegeman, Willem, et al. “Multi-Target Prediction : A Unifying View on Problems and Methods.” DATA MINING AND KNOWLEDGE DISCOVERY, vol. 33, no. 2, 2019, pp. 293–324, doi:10.1007/s10618-018-0595-5.
- 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. https://doi.org/10.1007/s10618-018-0595-5
- 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. https://doi.org/10.1007/s10618-018-0595-5.
- 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. doi:10.1007/s10618-018-0595-5.
- 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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