Inverse-Category-Frequency Based Supervised Term Weighting Schemes for Text Categorization *
Deqing Wang, Hui Zhang · 2013
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 in-tuition 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 experi-ments have shown that our proposed approaches are superior or comparable to six super-vised term weighting schemes and three traditional schemes in terms of macro-F1 and micro-F1.