Exploiting Class Label Frequencies for Text Classification

Abstract

Document classification is an example of Machine Learning (ML) in the form of Natural Language Processing (NLP). By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort. In the vast majority of document classification techniques a document is represented as a bag of words consisting of all the individual terms making up the document together with the number of times each term appears in the document. The number of term occurrences is known as local term frequencies and it is very common to make use of the local term frequencies at the price of some added information in the classification model. In this work, we extend our previous work on medical article classification [1,2] by simplifying the weighting scheme in the ranking process using class label frequencies to device a simple weighting formula inspired from traditional information retrieval task. We also evaluate the proposed approach using more research experimental data.  The method we propose here, called CLF KNN first, it uses a lexical approach to identify frequency terms in the document texts and then, it uses this information coupled with class label information in corpus in a sophisticated way to devise a weighting ranking scheme in classification decision process. The evaluation experiments on two collections: The Ohsumed collection of medical documents and the 20 Newsgroup messages collection, show that the proposed method significantly outperforms traditional KNN classification