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.