TY - JOUR
T1 - Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
AU - Jensen, Richard
AU - Shen, Qiang
N1 - R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12): 1457-1471. 2004.
PY - 2004
Y1 - 2004
N2 - 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.
AB - 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.
U2 - 10.1109/TKDE.2004.96
DO - 10.1109/TKDE.2004.96
M3 - Article
SN - 1041-4347
VL - 16
SP - 1457
EP - 1471
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 12
ER -