Review on Various Problem Transformation Methods for Classifying Multi-Label Data

Learning from multi-label and multi-target data is always more difficult than single-label data. The complexity is incurred by multiple labels assigned to a sample as well as to provide ranking among labels. The learning algorithms used for single-label data are not suitable to learn from multi-label and multi-target data. There are many applications where multi-label and multi-target data are present, for example, image classification, gene classification and document classification. In this paper, review of multi-label classification methods is described. To see correlation between some of these methods and their performance measures, experiments are performed on real multi-label datasets and results are presented.