Boosting KNN text classification accuracy by using supervised term weighting schemes

Abstract

The increasing availability of digital documents in the last decade has prompted the development of machine learning techniques to automatically classify and organize text documents. The majority of text classification systems rely on the vector space model, which represents the documents as vectors in the term space. Each vector component is assigned a weight that reflects the importance of the term in the document. Typically, these weights are assigned using an information retrieval (IR) approach, such as the famous tf-idf function. In this work, we study two weighting schemes based on information gain and chi-square statistics. These schemes take advantage of the category label information to weight the terms according to their distributions across the different categories. We show that using these supervised weights instead of conventional unsupervised weights can greatly improve the performance of the k-nearest neighbor (KNN) classifier. Experimental evaluations, carried out on multiple text classification tasks, demonstrate the benefits of this approach in creating accurate text classifiers.