Feature Selection (FS) is an ideal pre-processing stage to make supervised learning more effective and efficient. RELIEF_NCM, a variant of Relief, a non-parametric feature weighting algorithm in the literature developed to overcome the limitations of RELIEF_DISC. It is designed to consider nominal and continuous features and support multi-class problems. The RELIEF_NCM algorithm removes the irrelevant features from the dataset, but there may still be a possibility of redundant features that may hurt the performance of the classifiers. RedunSUn, a method that removes redundant features using Symmetric Uncertainty (SU), has been introduced in the research paper. The research article introduced a bi-stage FS algorithm to remove redundant and irrelevant features in the dataset by combining RELIEF_NCM and RedunSUn called RELSUn. This hybrid approach RELSun has been examined using eight real-time datasets from the UCI machine learning repository. The investigational outcomes reveal that RELSun outperforms RELIEF_NCM and state-of-the-art methods regarding classification accuracy, precision, and speed of Naïve Bayesian Classifier (NBC) with minimum selected attributes.