  Term weighting schemes often dominate the performance of many classifiers,
such as kNN, centroid-based classifier and SVMs. The widely used term weighting
scheme in text categorization, i.e., tf.idf, is originated from information
retrieval (IR) field. The intuition behind idf for text categorization seems
less reasonable than IR. In this paper, we introduce inverse category frequency
(icf) into term weighting scheme and propose two novel approaches, i.e., tf.icf
and icf-based supervised term weighting schemes. The tf.icf adopts icf to
substitute idf factor and favors terms occurring in fewer categories, rather
than fewer documents. And the icf-based approach combines icf and relevance
frequency (rf) to weight terms in a supervised way. Our cross-classifier and
cross-corpus experiments have shown that our proposed approaches are superior
or comparable to six supervised term weighting schemes and three traditional
schemes in terms of macro-F1 and micro-F1.
