Multi-Target Prediction: A Unifying View on Problems and Methods

TL;DR

This paper offers a comprehensive unifying framework for multi-target prediction, integrating various subfields and methods, and discusses key properties and future challenges in the field.

Contribution

It introduces a general framework that unifies diverse MTP problems and provides a structured overview of methods based on key distinguishing properties.

Findings

Unified view of MTP problems and methods

Identification of key properties for method suitability

Discussion of future research challenges

Abstract

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on 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.