Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kalpana, P.; Sumathi, P.; Balan, R. V. Siva; Padmapriya, S. Title A name given to the resource Optimizing the Performance of Nae Bayesian Classification Using RELSUn, a Bi-stage Feature Selection Algorithm Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1341 LNNS;pp.73-88 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5126-9_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5126-9_6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020818867?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020818867?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kalpana P., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Sumathi P., Department of Computer Science, VYSYA College, Tamil Nadu, Salem, India; Balan R.V.S., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Padmapriya S., Department of Computer Science Engineering, Dhanalakshmi Srinivasan University, Tamil Nadu, Tiruchirappalli, India Description An account of the resource 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 Nae Bayesian Classifier (NBC) with minimum selected attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Feature selection; RedunSUn; RELIEF; RELIEF_NCM; RELSun; Symmetric uncertainty Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965125-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online