We propose two entropy-based term weighting schemes (ie, tf.dc and tf.bdc) which measure the discriminating power of a term based on its global distributional ...

This paper proposes two entropy-based term weighting schemes which measure the discriminating power of a term based on its global distributional ...

To this end, we propose two entropy- based term weighting schemes (i.e., tf·dc and tf·bdc) which measure the discriminating power of a term based on its global.

We then propose a series of entropy-based term weighting schemes to measure the distinguishing power of a term in text categorization.

In this paper, we first systematically examine pros and cons of existing term weighting schemes in text categorization and explore the reasons why some schemes ...

We then propose a series of entropy-based term weighting schemes to measure the distinguishing power of a term in text categorization.

In text categorization, Vector Space Model (VSM) has been widely used for representing documents, in which a document is represented by a vector of terms.

By measuring the concentration that a term distributes across all categories in a corpus, a series of entropy-based term weighting schemes are proposed to ...

Wang et al. [19] proposed entropy-based term weighting schemes that use a term's global distributional concentration in the categories to measure its ...

To this end, we propose two entropy-based term weighting schemes (i.e., tf.dc and tf.bdc) which measure the discriminating power of a term based on its global ...