Multilabel Classification is a supervised learning problem in which a model is trained to assign each input document to a set of labels, where one or more labels may be associated with each document. It is different from multi-class classification, which only assigns a single label to each item in a given input.
Multilabel Classification can be used when the classes of the objects are hierarchical. For instance, when classifying an image of a car, it can be placed into the labels of “vehicle” and “car”. Here, the label “car” is a sub-label of the more general label “vehicle”. Multilabel Classification with hierarchical labels allows the given label to be more specific.
Multilabel Classification can also be more accurate when classifying complex data, since it allows for multiple labels and allows the machine to make choices based on all the labels associated with a given item. As an example, when classifying a picture of a vehicle, Multilabel Classification allows the machine to make choices based on the labels of “car”, “motorcycle”, “truck”, and so on.
Multilabel Classification can also be used in text-based categorization tasks, such as news articles. Here, a given article can be classified with the labels of the topic of the article, the region it is related to, and even the sentiment expressed in the text.
Multilabel Classification has become increasingly popular as the availability and availability of large datasets of labeled data has also increased. By taking advantage of Multilabel Classification’s ability to assign multiple labels to the data, higher accuracy can be achieved.
