A decision-theoretic roguth set model
Methodologies for intelligent systems, 5
Variable precision rough set model
Journal of Computer and System Sciences
Floating search methods in feature selection
Pattern Recognition Letters
Machine Learning
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
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
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Information Sciences—Informatics and Computer Science: An International Journal
A robust minimax approach to classification
The Journal of Machine Learning Research
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Constructive and axiomatic approaches of fuzzy approximation operators
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft data mining, computational theory of perceptions, and rough-fuzzy approach
Information Sciences: an International Journal - Special issue: Soft computing data mining
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Support Vector Machine Soft Margin Classifiers: Error Analysis
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing Data Analysis with Noise Removal
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction
Fundamenta Informaticae
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
An efficient ant colony optimization approach to attribute reduction in rough set theory
Pattern Recognition Letters
Generalized fuzzy rough sets determined by a triangular norm
Information Sciences: an International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
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
On characterization of intuitionistic fuzzy rough sets based on intuitionistic fuzzy implicators
Information Sciences: an International Journal
Feature selection based on loss-margin of nearest neighbor classification
Pattern Recognition
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Error detection and impact-sensitive instance ranking in noisy datasets
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Factor analysis latent subspace modeling and robust fuzzy clustering using t-distributions
IEEE Transactions on Fuzzy Systems
The model of fuzzy variable precision rough sets
IEEE Transactions on Fuzzy Systems
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Weighted k-nearest leader classifier for large data sets
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Stability analysis on rough set based feature evaluation
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Mining With Noise Knowledge: Error-Aware Data Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Robust fuzzy clustering of relational data
IEEE Transactions on Fuzzy Systems
On the generalization of soft margin algorithms
IEEE Transactions on Information Theory
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems
Fuzzy Sets and Systems
Robust fuzzy rough classifiers
Fuzzy Sets and Systems
Rule learning for classification based on neighborhood covering reduction
Information Sciences: an International Journal
A comparative study of rough sets for hybrid data
Information Sciences: an International Journal
Approximations and uncertainty measures in incomplete information systems
Information Sciences: an International Journal
Generalized intuitionistic fuzzy rough sets based on intuitionistic fuzzy coverings
Information Sciences: an International Journal
Rough set theory applied to lattice theory
Information Sciences: an International Journal
Relationships among generalized rough sets in six coverings and pure reflexive neighborhood system
Information Sciences: an International Journal
Soft Minimum-Enclosing-Ball Based Robust Fuzzy Rough Sets
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Information Sciences: an International Journal
Menger's theorem for fuzzy graphs
Information Sciences: an International Journal
Feature subset selection using separability index matrix
Information Sciences: an International Journal
A unified data mining solution for authorship analysis in anonymous textual communications
Information Sciences: an International Journal
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
A novel variable precision (θ,σ)-fuzzy rough set model based on fuzzy granules
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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The fuzzy dependency function proposed in the fuzzy rough set model is widely employed in feature evaluation and attribute reduction. It is shown that this function is not robust to noisy information in this paper. As datasets in real-world applications are usually contaminated by noise, robustness of data analysis models is very important in practice. In this work, we develop a new model of fuzzy rough sets, called soft fuzzy rough sets, which can reduce the influence of noise. We discuss the properties of the model and construct a new dependence function from the model. Then we use the function to evaluate and select features. The presented experimental results show the effectiveness of the new model.