Feature enrichment and selection for transductive classification on networked data

Çataltepe Z., SONMEZ A., SENLIOL B.

PATTERN RECOGNITION LETTERS, vol.37, pp.41-53, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 37
  • Publication Date: 2014
  • Doi Number: 10.1016/j.patrec.2013.07.009
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.41-53
  • Istanbul Technical University Affiliated: Yes


Networked data consist of nodes and links between the nodes which indicate their dependencies. Nodes have content features which are available for all the data; on the other hand, the labels are available only for the training data. Given the features for all the nodes and labels for training nodes, in transductive classification, labels for all remaining nodes are predicted. Learning algorithms that use both node content features and links have been developed. For example, collective classification algorithms use aggregated (such as sum or average of) labels of neighbors, in addition to node features, as inputs to a classifier. The classifier is trained using the training data only. When testing, since the neighbors' labels are used as classifier inputs, the labels for the test set need to be determined through an iterative procedure.