Publication 2004 Issue No. 12 - December Abstract - Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
December 2004 (vol. 16 no. 12)
pp. 1457-1471
 ASCII Text x Richard Jensen, Qiang Shen, "Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1457-1471, December, 2004.
 BibTex x @article{ 10.1109/TKDE.2004.96,author = {Richard Jensen and Qiang Shen},title = {Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches},journal ={IEEE Transactions on Knowledge and Data Engineering},volume = {16},number = {12},issn = {1041-4347},year = {2004},pages = {1457-1471},doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2004.96},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Knowledge and Data EngineeringTI - Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based ApproachesIS - 12SN - 1041-4347SP1457EP1471EPD - 1457-1471A1 - Richard Jensen, A1 - Qiang Shen, PY - 2004KW - Dimensionality reductionKW - feature selectionKW - feature transformationKW - rough selectionKW - fuzzy-rough selection.VL - 16JA - IEEE Transactions on Knowledge and Data EngineeringER -
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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Index Terms:
Dimensionality reduction, feature selection, feature transformation, rough selection, fuzzy-rough selection.
Citation:
Richard Jensen, Qiang Shen, "Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1457-1471, Dec. 2004, doi:10.1109/TKDE.2004.96