VLSI fuzzy chip and inference accelerator board systems
Fuzzy logic for the management of uncertainty
Variable precision rough set model
Journal of Computer and System Sciences
Variable precision extension of rough sets
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Experiments with Rough Sets Approach to Speech Recognition
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Induction of Decision Rules and Classification in the Valued Tolerance Approach
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Learning rough set classifiers from gene expressions and clinical data
Fundamenta Informaticae
Feature selection based on a modified fuzzy C-means algorithm with supervision
Information Sciences—Informatics and Computer Science: An International Journal
Handbook of data mining and knowledge discovery
Application of rule induction and rough sets to verification of magnetic resonance diagnosis
Fundamenta Informaticae
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Measures for evaluating the decision performance of a decision table in rough set theory
Information Sciences: an International Journal
Converse approximation and rule extraction from decision tables in rough set theory
Computers & Mathematics with Applications
Several approaches to attribute reduction in variable precision rough set model
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Comparative study of variable precision rough set model and graded rough set model
International Journal of Approximate Reasoning
Cancer data investigation using variable precision Rough set with flexible classification
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
International Journal of Approximate Reasoning
Information Sciences: an International Journal
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Objective: Rough set theory (RST) provides powerful methods for reduction of attributes and creation of decision rules, which have successfully been applied in numerous medical applications. The variable precision rough set model (VPRS model), an extension of the original rough set approach, tolerates some degree of misclassification of the training data. The basic idea of the VPRS model is to change the class information of those objects whose class information cannot be induced without contradiction from the available attributes. Thereafter, original methods of RST are applied. An approach of this model is presented that allows uncertain objects to change class information during the process of attribute reduction and rule generation. This method is referred to as variable precision rough set approach with flexible classification of uncertain objects (VPRS(FC) approach) and needs only slight modifications of the original VPRS model. Methods and material: To compare the VPRS model and VPRS(FC) approach both methods are applied to a clinical data set based on electroencephalogram of awake and anesthetized patients. For comparison, a second data set obtained from the UCI machine learning repository is used. It describes the shape of different vehicle types. Further well known feature selection methods were applied to both data sets to compare their results with the results provided by rough set based approaches. Results: The VPRS(FC) approach requires higher computational effort, but is able to achieve better reduction of attributes for noisy or inconsistent data and provides smaller rule sets. Conclusion: The presented approach is a useful method for substantial attribute reduction in noisy and inconsistent data sets.