An Effective Supervised Streamed Text Classification Approach for Mining Positive and Negative Examples

Abstract

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the field of text mining. This survey paper is based on effective classification of streamed data for text mining by PNLH & one-class classification SVM for text contained audit, we consider the problem of one-class classification of text streams with respect to concept drift where a large volume of documents arrives at a high speed and with change of user interests and data distribution. In this case, only a small number of positively labelled documents is available for training. And text classification without negative examples revisit, by this we propose a labelling heuristic called PNLH to tackle this problem. PNLH aims at extracting high quality positive examples and negative examples from U and our survey can be used on top of any existing classifiers.