What is multi-label feature selection?
Feature selection is an important task in machine learning, which can effectively reduce the dataset dimensionality by removing irrelevant and/or redundant features. To this end, two standard multi-label feature selection approaches, which transform the multi-label data into single-label data, are used.
How do you perform multi label classification?
These tasks are referred to as multiple label classification, or multi-label classification for short. In multi-label classification, zero or more labels are required as output for each input sample, and the outputs are required simultaneously. The assumption is that the output labels are a function of the inputs.
How is single label classification different from multi label classification?
The evaluation measures for single-label are usually different than for multi-label. Here in single-label classfication we use simple metrics such as precision, recall, accuracy, etc,. Say, in single-label classification, accuracy is just: In multi-label classification, a misclassification is no longer a hard wrong or right.
How is multi label classification used in computer vision?
Or multi-label classification of genres based on movie posters. (This enters the realm of computer vision.) In multi-label classification, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets.
When do labels belong to a hierarchical structure?
When the labels in a data set belong to a hierarchical structure then we call the task hierarchical classification. If each example is labelled with more than one node of the hierarchical structure, then the task is called hierarchical multi-label classification.
Can a two class problem be a multi label problem?
Traditional two-class and multi-class problems can both be cast into multi-label ones by restricting each instance to have only one label. On the other hand, the generality of multi-label problems inevitably makes it more difficult to learn.