Towards Multi Label Text Classification through Label Propagation

Classifying text data has been an active area of research for a long time. Text document is multifaceted object and often inherently ambiguous by nature. Multi-label learning deals with such ambiguous object. Classification of such ambiguous text objects often makes task of classifier difficult while assigning relevant classes to input document. Traditional single label and multi class text classification paradigms cannot efficiently classify such multifaceted text corpus. Through our paper we are proposing a novel label propagation approach based on semi supervised learning for Multi Label Text Classification. Our proposed approach models the relationship between class labels and also effectively represents input text documents. We are using semi supervised learning technique for effective utilization of labeled and unlabeled data for classification .Our proposed approach promises better classification accuracy and handling of complexity and elaborated on the basis of standard datasets such as Enron, Slashdot and Bibtex.

@article{Dharmadhikari2012,
  title     = {Towards Multi Label Text Classification through Label Propagation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {3},
  number    = {6},
  year      = {2012},
  publisher = {The Science and Information Organization},
  author    = {Shweta C Dharmadhikari and Maya Ingle and Parag Kulkarni},
  doi       = {10.14569/IJACSA.2012.030607},
  url       = {https://doi.org/10.14569/IJACSA.2012.030607}
}