Hierarchical Text Categorization in a Transductive Setting

Michelangelo Ceci
2008 2008 IEEE International Conference on Data Mining Workshops  
Transductive learning is the learning setting that permits to learn from "particular to particular" and to consider both labelled and unlabelled examples when taking classification decisions. In this paper, we investigate the use of transductive learning in the context of hierarchical text categorization. At this aim, we exploit a modified version of an inductive hierarchical learning framework that permits to classify documents in internal and leaf nodes of a hierarchy of categories. Experimental results on real world datasets are reported.
doi:10.1109/icdmw.2008.126 dblp:conf/icdm/Ceci08 fatcat:x2e423asqnfhjmqbxzp7zearu4