Nowadays, multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification. This paper introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multi-label classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.
Label Powerset learning (LP ) transforms a multi-label classification into a multi-class problem REF .
Multi-label classification: An overview
Int J Data Warehousing and Mining
Computer Science