Traditional methods in machine learning and statistics provide data-driven models for predicting one-dimensional targets, such as binary outputs in classification and real-valued outputs in regression. In recent years, novel application domains have triggered fundamental research on more complicated problems where multi-target predictions are required. Such problems arise in diverse application domains, such as document categorization, tag recommendation of images, videos and music, information retrieval, medical decision making, drug discovery, marketing, biology, geographical information systems, etc. According to a general definition, the targets in multi-target prediction problems might be characterized by diverse data types, such as binary, nominal, ordinal and real-valued variables, but also rankings and relational structures, representing different entities of interest. Moreover, they often exhibit specific relationships, in the sense of being structured as a tree-shaped hierarchy or a directed acyclic graph, or being characterized by mutual exclusion, parent-child and other types of relationships. Specific multi-target prediction problems have been studied in a variety of subfields of machine learning and statistics, such as multi-label classification (prediction of multiple binary targets), multivariate regression (prediction of multiple numerical targets), sequence learning (ordered targets of varying length), structured output prediction (targets with inherent structure), preference learning (prediction of a preference relation between multiple targets, as in label ranking), multi-task learning (prediction of multiple targets in different but related domains) and collective learning (prediction for dependent observations).