The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
On domain knowledge and feature selection using a support vector machine
Pattern Recognition Letters
Machine Learning
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Information Sciences—Informatics and Computer Science: An International Journal
Image Representations and Feature Selection for Multimedia Database Search
IEEE Transactions on Knowledge and Data Engineering
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An axiomatic characterization of a fuzzy generalization of rough sets
Information Sciences—Informatics and Computer Science: An International Journal
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches
IEEE Transactions on Knowledge and Data Engineering
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
Combined SVM-Based Feature Selection and Classification
Machine Learning
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
FS_SFS: A novel feature selection method for support vector machines
Pattern Recognition
Short communication: Uncertainty measures for fuzzy relations and their applications
Applied Soft Computing
On Representing and Generating Kernels by Fuzzy Equivalence Relations
The Journal of Machine Learning Research
Attribute reduction based on evidence theory in incomplete decision systems
Information Sciences: an International Journal
On fuzzy approximation operators in attribute reduction with fuzzy rough sets
Information Sciences: an International Journal
Generalized fuzzy rough sets determined by a triangular norm
Information Sciences: an International Journal
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications
International Journal of Approximate Reasoning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Fuzzy-Rough Sets Assisted Attribute Selection
IEEE Transactions on Fuzzy Systems
Attributes Reduction Using Fuzzy Rough Sets
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
A comparative study of rough sets for hybrid data
Information Sciences: an International Journal
Geometrical interpretation and applications of membership functions with fuzzy rough sets
Fuzzy Sets and Systems
Quantitative analysis for covering-based rough sets through the upper approximation number
Information Sciences: an International Journal
A novel and better fitness evaluation for rough set based minimum attribute reduction problem
Information Sciences: an International Journal
Menger's theorem for fuzzy graphs
Information Sciences: an International Journal
Attribute reduction for dynamic data sets
Applied Soft Computing
FRPS: A Fuzzy Rough Prototype Selection method
Pattern Recognition
A novel method for attribute reduction of covering decision systems
Information Sciences: an International Journal
Structure of feature spaces related to fuzzy similarity relations as kernels
Fuzzy Sets and Systems
An improved algorithm for calculating fuzzy attribute reducts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Fuzzy rough sets are considered as an effective tool to deal with uncertainty in data analysis, and fuzzy similarity relations are used in fuzzy rough sets to calculate similarity between objects. On the other hand in kernel tricks, a kernel maps data into a higher dimensional feature space where the resulting structure of the learning task is linearly separable, while the kernel is the inner product of this feature space and can also be viewed as a similarity function. It has been reported there is an overlap between family of kernels and collection of fuzzy similarity relations. This fact motivates the idea in this paper to use some kernels as fuzzy similarity relations and develop kernel based fuzzy rough sets. First, we consider Gaussian kernel and propose Gaussian kernel based fuzzy rough sets. Second we introduce parameterized attribute reduction with the derived model of fuzzy rough sets. Structures of attribute reduction are investigated and an algorithm with discernibility matrix to find all reducts is developed. Finally, a heuristic algorithm is designed to compute reducts with Gaussian kernel fuzzy rough sets. Several experiments are provided to demonstrate the effectiveness of the idea.