TY  - JOUR
ID  - waegeman19multitarget
T1  - Multi-target prediction: a unifying view on problems and methods
A1  - Waegeman, Willem
A1  - Dembczy\'nski, Krzysztof
A1  - Hüllermeier, Eyke
JA  - Data Mining and Knowledge Discovery
Y1  - 2019
VL  - 33
IS  - 2
SP  - 293
EP  - 324
SN  - 1573-756X
N1  - 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.
UR  - https://doi.org/10.1007/s10618-018-0595-5
M2  - doi: 10.1007/s10618-018-0595-5
ER  -