DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Automated text categorisation has been considered as a vital method to manage and process vast amount of documents in digital form that are widespread and continuously increasing.Traditional classification problems are usually associated with a single label.Text Categorization uses Multi-label Learning which is a form of supervised learning where the classification algorithm is required to learn from a set of instances, each instance can belong to multiple classes and then be able to predict a set of class labels for a new instance. Multi-label classification methods have been increasingly used in modern applications such as music categorization, functional genomics (gene protein interactions) and semantic annotation of images besides document filtering, email classification and Web search. Multi-label classification methods can be broadly classified asProblem transformation and Algorithm adaptation. This paper presents anoverview of single-label text classificationand an analysis ofsome multilabel classification methods.
