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

• Published in: Data mining and knowledge discovery Vol. 33; no. 2; pp. 293 - 324
• Main Authors: Waegeman, Willem, Dembczyński, Krzysztof, Hüllermeier, Eyke
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2019; Springer Nature B.V
• Subjects: Academic Surveys and Tutorials; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Identification methods; Information Storage and Retrieval; Machine learning; Multivariate analysis; Physics; Regression analysis; Statistics for Engineering; Zero-shot learning; Collaborative filtering; Multi-label classification; Multivariate regression; Pairwise learning; Dyadic prediction; Multi-task learning
• ISSN: 1384-5810; 1573-756X
• DOI: 10.1007/s10618-018-0595-5

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.