A Multiclassifier based Document Categorization System: profiting from the Singular Value Decomposition Dimensionality Reduction Technique

In this paper we present a multiclassifier approach for multilabel document classification problems, where a set of k-NN classifiers is used to predict the category of text documents based on different training subsampling databases. These databases are obtained from the original training database by random subsampling. In order to combine the predictions generated by the multiclassifier, Bayesian voting is applied. Through all the classification process, a reduced dimension vector representation obtained by Singular Value Decomposition (SVD) is used for training and testing documents. The good results of our experiments give an indication of the potentiality of the proposed approach.