Feature Transformation and Reduction for Text Classification

Text classification is an important tool for many applications, in supervised, semi-supervised, and unsupervised scenarios. In order to be processed by machine learning methods, a text (document) is usually represented as a bag of-words (BoW). A BoW is a large vector of features (usually stored as floating point values), which represent the relative frequency of occurrence of a given word/term in each document. Typically, we have a large number of features, many of which may be non-informative for classification tasks and thus the need for feature transformation, reduction, and selection arises. In this paper, we propose two efficient algorithms for feature transformation and reduction for BoW-like representations. The proposed algorithms rely on simple statistical analysis of the input pattern, exploiting the BoW and its binary version. The algorithms are evaluated with support vector machine (SVM) and AdaBoost classifiers on standard benchmark datasets. The experimental results show the adequacy of the reduced/transformed binary features for text classification problems as well as the improvement on the test set error rate, using the proposed methods.