Hierarchical Text Categorization through a Vertical Composition of Classifiers
Andrea Addis, Giuliano Armano, Francesco Mascia and Eloisa Vargiu
The 10th Congress of the Italian Association for Artificial Intelligence (AIIA 2007)
Roma, Italy, September 10-13, 2007
Abstract
In this paper we present a hierarchical approach to text categorization aimed at improving the performances of the corresponding tasks. The proposed approach is explicitly devoted to cope with the problem related to the unbalance between relevant and non relevant inputs. The technique has been implemented and tested by resorting to a multiagent system aimed at performing information retrieval tasks. Experiments, performed on RCV1-v2, point out the validity of the approach.