Multi-Label Classification

Multi-Label Classification is the definition of concepts for the quantification of the multi-label nature of a data set. It is a binary relevance, which simply trains a classifier for each label independently. Multi-Label Classification should not be confused with multiclass classification, which is the problem of categorizing instances into one of more than two classes. There are two main methods for tackling the multi-label classification problem: problem transformation methods and algorithm adaptation methods.