Hybrid Classifiers for Improved Semantic Subspace Learning of News Documents

The volume and diversity of documents available in today's world is increasing daily. It is therefore difficult for a single classifier to efficiently handle multi-level categorization of such a varied document space. In this paper we analyse methods to enhance the efficiency of a single classifier for two-level classification by combining it with classifiers of other types. We use the maximum significance value as an indicator for the subspace of a test document. We represent the documents using the conditional significance vector which increases the distinction between classes within a subspace. Our experiments show that dividing a document space into different semantic subspaces increases the efficiency of such hybrid classifier combinations. Applying different types of classifiers on different subspaces substantially improves overall learning.

@InProceedings{OWT11a, 
 	 author =  {Oakes, Michael Philip and Wermter, Stefan and Tripathi, Nandita},  
 	 title = {Hybrid Classifiers for Improved Semantic Subspace Learning of News Documents}, 
 	 booktitle = {Proceedings of the 11th International Conference on Hybrid Intelligent Systems (HIS 2011)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {28--33},
 	 year = {2011},
 	 month = {Dec},
 	 publisher = {IEEE},
 	 doi = {10.1109/HIS.2011.6122075}, 
 }