IMPACT OF TERM DEPENDENCY AND CLASS IMBALANCE ON THE PERFORMANCE OF FEATURE RANKING METHODS

Abstract

Feature ranking is widely employed to deal with high dimensionality in text classification. The main advantage of feature ranking methods is their low cost and simple algorithms. However, they suffer from some drawbacks which cause low performance compared to wrapper approach feature selection methods. In this paper, three major drawbacks of feature ranking methods are discussed. First, we show that feature ranking methods are highly problem dependent. For designing an effective feature ranking method and appropriate ranking threshold, we need background knowledge including the data set characteristics as well as the classifier to be used. Second, the feature ranking methods are univariate functions, while the nature of text classification is multivariate. It means that in these methods, correlation between terms is ignored. Finally, they fail in multiple class problems with unbalanced class distribution because they pay more attention to the simpler and larger classes. In this paper, these drawbacks, especially the last two issues, are experimentally investigated using a set of extensive numerical experiments with several data sets and feature scoring measures.