A Fast Multivariate Symmetrical Uncertainty Based Heuristic for High Dimensional Feature Selection.

Abstract

In classification tasks the increase in the number of dimensions of a data makes the learning process harder. In this context feature selection usually allows to induce simpler classifier models while keeping the accuracy. However, some factors, such as the presence of irrelevant and redundant features, make the feature selection process challenging.