  In this paper, we introduce a new measure called Term_Class relevance to
compute the relevancy of a term in classifying a document into a particular
class. The proposed measure estimates the degree of relevance of a given term,
in placing an unlabeled document to be a member of a known class, as a product
of Class_Term weight and Class_Term density; where the Class_Term weight is the
ratio of the number of documents of the class containing the term to the total
number of documents containing the term and the Class_Term density is the
relative density of occurrence of the term in the class to the total occurrence
of the term in the entire population. Unlike the other existing term weighting
schemes such as TF-IDF and its variants, the proposed relevance measure takes
into account the degree of relative participation of the term across all
documents of the class to the entire population. To demonstrate the
significance of the proposed measure experimentation has been conducted on the
20 Newsgroups dataset. Further, the superiority of the novel measure is brought
out through a comparative analysis.
