A decision-theoretic roguth set model
Methodologies for intelligent systems, 5
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Information Sciences—Informatics and Computer Science: An International Journal
The smallest enclosing ball of balls: combinatorial structure and algorithms
Proceedings of the nineteenth annual symposium on Computational geometry
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
An Approach for Fuzzy-Rough Sets Attributes Reduction via Mutual Information
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Outlier Detection with the Kernelized Spatial Depth Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
The model of fuzzy variable precision rough sets
IEEE Transactions on Fuzzy Systems
Stability analysis on rough set based feature evaluation
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Fuzzy rough set theory was introduced as a useful mathematical tool to handle real-valued data. Unluckily, its sensitivity to noise has a great impact on the application in real world. So it is necessary to enhance the robustness of fuzzy rough sets. In this work, based on the minimum enclosing ball problem we introduce a robust model of fuzzy rough sets. In addition, we define a robust fuzzy dependency function and apply it to evaluate features corrupted by noise. Experimental results show that the new model is robust to noise.