k-NN Text Categorization Method Based on Transferable Belief Model

The k-nearest neighbors(k-NN) categorization method is simple and effective in text categorization. The uncertainty of training documents and classes border would appear in multi-class categorization, because of the overlapping of classes and the lack of features. But the conventional k-NN method is unsuitable to deal with this uncertainty. To this problem, a k-NN text categorization method based on the transferable belief model(TBM) is presented in the paper, It´s convenient to make decision about the true class membership of a text to be classified through the application of the pignistic transformation. The experiment shows the method improve the precision and recall of text categorization.