Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches
Top Cited Papers
Open Access
- 1 November 2004
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. 16 (12), 1457-1471
- https://doi.org/10.1109/tkde.2004.96
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.Keywords
This publication has 26 references indexed in Scilit:
- Selecting informative features with fuzzy-rough sets and its application for complex systems monitoringPattern Recognition, 2004
- Fuzzy–rough attribute reduction with application to web categorizationFuzzy Sets and Systems, 2004
- Reduction algorithms based on discernibility matrix: The ordered attributes methodJournal of Computer Science and Technology, 2001
- Rough set-aided keyword reduction for text categorizationApplied Artificial Intelligence, 2001
- Constructive and algebraic methods of the theory of rough setsInformation Sciences, 1998
- Neural-network feature selectorIEEE Transactions on Neural Networks, 1997
- Feature selection for classificationIntelligent Data Analysis, 1997
- ROUGH SET REDUCTION OF ATTRIBUTES AND THEIR DOMAINS FOR NEURAL NETWORKSComputational Intelligence, 1995
- Learning Boolean concepts in the presence of many irrelevant featuresArtificial Intelligence, 1994
- Variable precision rough set modelJournal of Computer and System Sciences, 1993