Fuzzy entropy and conditioning
Information Sciences: an International Journal
C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Efficient Visual Recognition Using the Hausdorff Distance
Efficient Visual Recognition Using the Hausdorff Distance
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Searching for Relational Patterns in Data
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
The Hausdor_ Distance Measure for Feature Selection in Learning Applications
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Journal of the American Society for Information Science and Technology
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Applying indiscernibility attribute sets to knowledge reduction
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Fuzzy SVM for content-based image retrieval: a pseudo-label support vector machine framework
IEEE Computational Intelligence Magazine
A soft relevance framework in content-based image retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Fuzzy Sets and Rough Sets for Scenario Modelling and Analysis
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
An Efficient Gene Selection Algorithm Based on Tolerance Rough Set Theory
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Are more features better? a response to attributes reduction using fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Fuzzy-rough approaches for mammographic risk analysis
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
Hybrid mammogram classification using rough set and fuzzy classifier
Journal of Biomedical Imaging
Two novel feature selection methods based on decomposition and composition
Expert Systems with Applications: An International Journal
Research on the model of rough set over dual-universes
Knowledge-Based Systems
The Knowledge Engineering Review
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Evolutionary tolerance-based gene selection in gene expression data
Transactions on rough sets XIV
Data Mining and Knowledge Discovery
Probabilistic rough set over two universes and rough entropy
International Journal of Approximate Reasoning
Approximations and uncertainty measures in incomplete information systems
Information Sciences: an International Journal
Extended rough set-based attribute reduction in inconsistent incomplete decision systems
Information Sciences: an International Journal
An accelerator for attribute reduction based on perspective of objects and attributes
Knowledge-Based Systems
International Journal of Approximate Reasoning
A novel feature selection method and its application
Journal of Intelligent Information Systems
A fast feature selection approach based on rough set boundary regions
Pattern Recognition Letters
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Of all of the challenges which face the effective application of computational intelligence technologies for pattern recognition, dataset dimensionality is undoubtedly one of the primary impediments. In order for pattern classifiers to be efficient, a dimensionality reduction stage is usually performed prior to classification. Much use has been made of rough set theory for this purpose as it is completely data-driven and no other information is required; most other methods require some additional knowledge. However, traditional rough set-based methods in the literature are restricted to the requirement that all data must be discrete. It is therefore not possible to consider real-valued or noisy data. This is usually addressed by employing a discretisation method, which can result in information loss. This paper proposes a new approach based on the tolerance rough set model, which has the ability to deal with real-valued data whilst simultaneously retaining dataset semantics. More significantly, this paper describes the underlying mechanism for this new approach to utilise the information contained within the boundary region or region of uncertainty. The use of this information can result in the discovery of more compact feature subsets and improved classification accuracy. These results are supported by an experimental evaluation which compares the proposed approach with a number of existing feature selection techniques.