Optimizing the Performance of Nae Bayesian Classification Using RELSUn, a Bi-stage Feature Selection Algorithm
- Title
- Optimizing the Performance of Nae Bayesian Classification Using RELSUn, a Bi-stage Feature Selection Algorithm
- Creator
- Kalpana, P.; Sumathi, P.; Balan, R. V. Siva; Padmapriya, S.
- Description
- 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.
- Source
- Lecture Notes in Networks and Systems;Volume;1341 LNNS;pp.73-88
- Date
- 01-01-2026
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Feature selection; RedunSUn; RELIEF; RELIEF_NCM; RELSun; Symmetric uncertainty
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 23673370; ISBN: 978-981965125-2;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kalpana, P.; Sumathi, P.; Balan, R. V. Siva; Padmapriya, S., “Optimizing the Performance of Nae Bayesian Classification Using RELSUn, a Bi-stage Feature Selection Algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25558.