Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Adaptive Noise Equalization and Image Analysis in Mammography
IPMI '93 Proceedings of the 13th International Conference on Information Processing in Medical Imaging
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Finding Reducts in Composed Information Systems
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Journal of the American Society for Information Science and Technology
International Journal of Hybrid Intelligent Systems
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Rough Sets in Hybrid Soft Computing Systems
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Rule-Based Assistance to Brain Tumour Diagnosis Using LR-FIR
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Modelling of rough-fuzzy classifier
WSEAS TRANSACTIONS on SYSTEMS
Fuzzy rough sets hybrid scheme for motion and scene complexity adaptive deinterlacing
Image and Vision Computing
Systems modelling on the basis of rough and rough-fuzzy approach
WSEAS Transactions on Information Science and Applications
Neuroeconomics: Yet Another Field Where Rough Sets Can Be Useful?
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
FRSVMs: Fuzzy rough set based support vector machines
Fuzzy Sets and Systems
Investigation into effectiveness of rough sets in prediction of enzyme and protein structure classes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Fuzzy preference based rough sets
Information Sciences: an International Journal
Neural networks and other machine learning methods in cancer research
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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
Expert Systems with Applications: An International Journal
Effective recognition of MCCs in mammograms using an improved neural classifier
Engineering Applications of Artificial Intelligence
Detection of masses in mammogram images using CNN, geostatistic functions and SVM
Computers in Biology and Medicine
Computers in Biology and Medicine
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Multiscale roughness measure for color image segmentation
Information Sciences: an International Journal
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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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This paper introduces a hybrid scheme that combines the advantages of fuzzy sets and rough sets in conjunction with statistical feature extraction techniques. An application of breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. The introduced scheme starts with fuzzy image processing as pre-processing techniques to enhance the contrast of the whole image; to extracts the region of interest and then to enhance the edges surrounding the region of interest. A subsequently extract features from the segmented regions of the interested regions using the gray-level co-occurrence matrix is presented. Rough sets approach for generation of all reducts that contains minimal number of attributes and rules is introduced. Finally, these rules can then be passed to a classifier for discrimination for different regions of interest to test whether they are cancer or non-cancer. To measure the similarity, a new rough set distance function is presented. The experimental results show that the hybrid scheme applied in this study perform well reaching over 98% in overall accuracy with minimal number of generated rules. (This paper was not presented at any IFAC meeting).