Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection

In these spaces of high dimensionality, feature selection is a way to exclude those irrelevant and redundant features, whose presence might complicate the task of knowledge discovery. In classification tasks, a feature is considered irrelevant if it contains no information about the class and therefore it is not necessary at all for the predictive task. Besides, it is widely accepted that two features are redundant if their values are correlated. There are several well known measures that compare features and determine their importance, such as the symmetrical uncertainty (SU)[2]. SU is a measure based on information that uses entropy and conditional entropy values to determine the correlation between pairs of features.