Combining feature ranking for text classification

Abstract

Feature ranking is one of the dimensionality reduction methods. Because of its simplicity and low cost, it is widely used in text classification. One problem with feature ranking methods is their non-robust behavior when applied to different data sets. In other words, the feature ranking methods behave differently from one data set to the other. The problem is more complex when we consider that the performance of feature ranking methods is different when being used by different classifiers. In this paper, a new method based on combining feature rankings is proposed to find the best features among a set of feature rankings. Four preferential voting method are employed to combine feature rankings obtained by eight well-known ranking measures. According to the results, combining methods can offer reliable results that are very close to the best solution without the need to use a classifier. The proposed method is applied to the text classification problem and evaluated on three well-known data sets using SVM classifier.