A feature selection method based on term frequency difference and positive weighting factor

Firstly, a new concept of term frequency difference factor is proposed to balance the influences of term frequency and document frequency on feature selection. Secondly, the idea of positive weighting factor is advanced to balance the roles of the document frequency in the positive and negampared with six popular algorithms on six datasets using two classifiers of Naive Bayes and Support tive categories. And finally, a new feature selection algorithm based on term frequency difference and positive weighting factor, PWTF-TCM, is presented based on the two above concepts. In the experiments, PWTF-TCM is coVector Machines. The experimental results show that PWTF-TCM outperforms by 75% for Macro-F and 58.33% for Micro-F. In addition, PWTF-TCM improves the classification accuracy by 4.58% compared with Trigonometric comparison measure.