document: A refinement approach to handling model misfit in text categorization Text categorization or classification is the automated assigning of text documents to pre-defined classes based on their contents . This problem has been studied in information retrieval , machine learning and data mining . So far , many effective techniques have been proposed . However , most techniques are based on some underlying models and\/or assumptions . When the data fits the model well , the classification accuracy will be high . However , when the data does not fit the model well , the classification accuracy can be very low . In this paper , we propose a refinement approach to dealing with this problem of model misfit . We show that we do not need to change the classification technique itself ( or its underlying model ) to make it more flexible . Instead , we propose to use successive refinements of classification on the training data to correct the model misfit . We apply the proposed technique to improve the classification performance of two simple and efficient text classifiers , the Rocchio classifier and the naïve Bayesian classifier . These techniques are suitable for very large text collections because they allow the data to reside on disk and need only one scan of the data to build a text classifier . Extensive experiments on two benchmark document corpora show that the proposed technique is able to improve text categorization accuracy of the two techniques dramatically . In particular , our refined model is able to improve the naïve Bayesian or Rocchio classifier 's prediction performance by 45 % on average .
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