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

• Waegeman, Willem ^[1] ; Dembczyński, Krzysztof ^[2] ; Hüllermeier, Eyke ^[3]
  1. [1] Ghent University

     Ghent University

     Arrondissement Gent, Bélgica

     
  2. [2] Poznań University of Technology

     Poznań University of Technology

     Poznań, Polonia

     
  3. [3] University of Paderborn

     University of Paderborn

     Kreis Paderborn, Alemania

     

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• Localización: Data mining and knowledge discovery, ISSN 1384-5810, Vol. 33, Nº 2, 2019, págs. 293-324
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.