Back to Search
Start Over
Granule-specific feature selection for continuous data classification using neighborhood rough sets.
- Source :
-
Expert Systems with Applications . Mar2024:Part C, Vol. 238, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Neighborhood rough set theories are commonly used in global feature selection to achieve high performance in continuous data classification. However, selecting a single feature subset to represent the entire dataset may degrade the performance when there are intra-class dissimilarities among objects. Therefore, this paper proposes a novel feature-selection method, Granule-specific Feature Selection (GFS) to select local feature subsets for continuous data classification. The feature selection approach constitutes a novel feature selection algorithm and a novel feature evaluation function and uses existing approaches for granule identification and classification with some adjustments. The neighborhood rough set theories are used in granule (subclass) identification within each class when there are no subclass label information available in the training data, while an improved k-Nearest Neighbors approach is used in classification with granule-specific feature subsets. Experimental results show GFS outperforms most of the global, class-specific, and local feature selection baselines in terms of classification performance. • Granule-specific feature selection using neighborhood rough set theories. • Feature selection while managing intra-class dissimilarities of objects. • Handle data uncertainty of continuous data when selecting features. • Improve classification performance using granule-specific feature subsets. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FEATURE selection
*ROUGH sets
*NEIGHBORHOODS
*K-nearest neighbor classification
Subjects
Details
- Language :
- English
- ISSN :
- 09574174
- Volume :
- 238
- Database :
- Academic Search Index
- Journal :
- Expert Systems with Applications
- Publication Type :
- Academic Journal
- Accession number :
- 173705949
- Full Text :
- https://doi.org/10.1016/j.eswa.2023.121765