Comparison of feature ranking methods based on information entropy

A comparison between five feature ranking methods based on entropy is presented on artificial and real datasets. Feature ranking method using χ² statistics gives results that are very similar to the entropy-based methods. The quality of feature rankings obtained by these methods is evaluated using the decision tree and the nearest neighbor classifier with growing number of most important features. Significant differences are found in some cases, but there is no single best index that works best for all data and all classifiers. Therefore to be sure that a subset of features giving highest accuracy has been selected requires the use of many different indices.