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A multiclass/multilabel document categorization system: Combining multiple classifiers in a reduced dimension
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DOI：10.1016/j.asoc.2011.06.002.png)
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Abstract
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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. (C) 2011 Elsevier B. V. All rights reserved.
Keywords:
Document categorization
Vector space models
Multiclassifiers
Distance based classifiers
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6.6
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1.4W
Citations:
4.8W
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