A multiclass/multilabel document categorization system: Combining multiple classifiers in a reduced dimension

This article presents a multiclassifier approach for multiclass/multilabel document categorization problems. For the categorization process, we use a reduced vector representation obtained by SVD for training and testing documents, and a set of k-NN classifiers to predict the category of test documents; each k-NN classifier uses a reduced database subsampled from the original training database. To perform multilabeling classifications, a new approach based on Bayesian weighted voting is also presented. The good results obtained in the experiments give an indication of the potential of the proposed approach.

► Multiclassifier approach for multiclass/multilabel document categorization problems. ► A reduced vector representation obtained by SVD is used to represent documents. ► A set of k-NN classifiers is used to predict the category of test documents. ► Bayesian weighted voting is used to perform multilabeling classifications. ► The classifier was evaluated for the Reuters-21578 ModApte split testing collection.