Majority of the stored data contain irrelevant and redundant features that affect the classification performance. Therefore, feature selection (FS) is essential to remove both the redundant and ir- relevant features and consequently improved the classification performance. FS can be either fil- ter or wrapper. In wrapper FS, a classifier is used to measure the accuracy of the selected fea- tures but its computationally expensive and not favourable on large dimensional datasets.