C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Fuzzy rough sets hybrid scheme for breast cancer detection
Image and Vision Computing
Fuzzy-rough nearest neighbor algorithms in classification
Fuzzy Sets and Systems
On fuzzy approximation operators in attribute reduction with fuzzy rough sets
Information Sciences: an International Journal
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
Feature selection for aiding glass forensic evidence analysis
Intelligent Data Analysis
Fuzzy-Rough set based nearest neighbor clustering classification algorithm
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
A Novel Breast Tissue Density Classification Methodology
IEEE Transactions on Information Technology in Biomedicine
Attributes Reduction Using Fuzzy Rough Sets
IEEE Transactions on Fuzzy Systems
The Knowledge Engineering Review
Core-generating discretization for rough set feature selection
Transactions on rough sets XIII
A tree classifier for automatic breast tissue classification based on BIRADS categories
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A quantifier-based fuzzy classification system for breast cancer patients
Artificial Intelligence in Medicine
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The accuracy of methods for the assessment of mammographic risk analysis is heavily related to breast tissue characteristics. Previous work has demonstrated considerable success in developing an automatic breast tissue classification methodology which overcomes this difficulty. This paper proposes a unified approach for the application of a number of rough and fuzzy-rough set methods to the analysis of mammographic data. Indeed this is the first time that fuzzy-rough approaches have been applied to this particular problem domain. In the unified approach detailed here feature selection methods are employed for dimensionality reduction developed using rough sets and fuzzy-rough sets. A number of classifiers are then used to examine the data reduced by the feature selection approaches and assess the positive impact of these methods on classification accuracy. Additionally, this paper also employs a new fuzzy-rough classifier based on the nearest neighbour classification algorithm. The novel use of such an approach demonstrates its efficiency in improving classification accuracy for mammographic data, as well as considerably removing redundant, irrelevant, and noisy features. This is supported with experimental application to two well-known datasets. The overall result of employing the proposed unified approach is that feature selection can identify only those features which require extraction. This can have the positive effect of increasing the risk assessment accuracy rate whilst additionally reducing the time required for expert scrutiny, which in-turn means the risk analysis process is potentially quicker and involves less screening.