Communications of the ACM
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Discovery through rough set theory
Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Intelligent Data Analysis in Medicine and Pharmacology
Intelligent Data Analysis in Medicine and Pharmacology
Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
A New Approach to Fuzzy Classifier Systems
Proceedings of the 5th International Conference on Genetic Algorithms
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
The training of neural classifiers with condensed datasets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy rough sets hybrid scheme for breast cancer detection
Image and Vision Computing
EROS: Ensemble rough subspaces
Pattern Recognition
Rough sets data analysis in knowledge discovery: a case of Kuwaiti diabetic children patients
Advances in Fuzzy Systems - Regular issue
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Exploring the boundary region of tolerance rough sets for feature selection
Pattern Recognition
Attribute dependency functions considering data efficiency
International Journal of Approximate Reasoning
Analysis on classification performance of rough set based reducts
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Expert Systems with Applications: An International Journal
Attribute reduction based expected outputs generation for statistical software testing
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
A rough set based model to rank the importance of association rules
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Introducing a rule importance measure
Transactions on Rough Sets V
Extended fuzzy c-means: an analyzing data clustering problems
Cluster Computing
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Rough set theory is a relatively new intelligent technique used in the discovery of data dependencies; it evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. Moreover, it is being used for the extraction of rules from databases. In this paper, we present a rough set approach to attribute reduction and generation of classification rules from a set of medical datasets. For this purpose, we first introduce a rough set reduction technique to find all reducts of the data that contain the minimal subset of attributes associated with a class label for classification. To evaluate the validity of the rules based on the approximation quality of the attributes, we introduce a statistical test to evaluate the significance of the rules. Experimental results from applying the rough set approach to the set of data samples are given and evaluated. In addition, the rough set classification accuracy is also compared to the well-known ID3 classifier algorithm. The study showed that the theory of rough sets is a useful tool for inductive learning and a valuable aid for building expert systems.