Adaptable Term Weighting Framework for Text Classification

In text classification, term frequency and term co-occurrence factors are dominantly used in weighting term features. Category relevance factors have recently been used to propose term weighting approaches. However, these approaches are mainly based on their own-designed text classifiers to adapt to category information, where the advantages of popular text classifiers have been ignored. This paper proposes a term weighting framework for text classification tasks. The framework firstly inherits the benefits of provided category information to estimate the weighting of features. Secondly, based on the feedback information, it is able to continuously adjust feature weightings to find the best representations for documents. Thirdly, the framework robustly makes it possible to work with different text classifiers on classifying the text representations, based on category information. On several corpora with SVM classifier, experiments show that given predicted information from TFxIDF method as initial status, the proposed approach leverages accuracy results and outperforms current text classification approaches.