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

作者：Zelaia A^*; Alegria I; Arregi O; Sierra B

来源：Applied Soft Computing, 2011, 11(8): 4981-4990.

DOI：10.1016/j.asoc.2011.06.002

• Document categorization
• Vector space models
• Multiclassifiers
• Distance based classifiers

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.

• 出版日期2011-12