Multi-label classification and problem transformation

In previous sections, we discussed binary classification, in which each instance must be assigned to one of two classes, and multi-class classification, in which each instance must be assigned to one of a set of classes. The final type of classification problem that we will discuss is multi-label classification, in which each instance can be assigned a subset of the set of classes. Examples of multi-label classification include assigning tags to messages posted to a forum and classifying objects present in an image. There are two groups of approaches for multi-label classification.

Problem transformation methods are techniques that cast the original multi-label problem as a set of single-label classification problems. The first problem transformation method that we will review converts each set of labels encountered in the training data to a single label. For example, consider a multi-label classification problem in which news articles...