The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches
Abstract
Semantics-preserving dimensionality reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition, and signal processing. This has found successful application in tasks that involve data sets containing huge numbers of features (in the order of tens of thousands), which would be impossible to process further. Recent examples include text processing and Web content classification. One of the many successful applications of rough set theory has been to this feature selection area. This paper reviews those techniques that preserve the underlying semantics of the data, using crisp and fuzzy rough set-based methodologies. Several approaches to feature selection based on rough set theory are experimentally compared. Additionally, a new area in feature selection, feature grouping, is highlighted and a rough set-based feature grouping technique is detailed.
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Cites
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Cited by
Pawlak, Z., & Skowron, A. (2007) · Information Sciences · doi:10.1016/j.ins.2006.06.003 · Cited by 2,070
Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface
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Neighborhood rough set based heterogeneous feature subset selection
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A Fitting Model for Feature Selection With Fuzzy Rough Sets
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Metadata
- Authors
- Jensen, R., & Shen, Q.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2004-12
- Volume
- 16
- Issue
- 12
- Pages
- 1457-1471
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2168523997
- Cited by
- 667
- References
- 45
- FWCI
- 20.75
- Type
- journal article
- Language
- en
- Open access
- closed
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