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
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
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
GMDH-based feature ranking and selection for improved classification of medical data
Journal of Biomedical Informatics
A Predictive Analysis on Medical Data Based on Outlier Detection Method Using Non-Reduct Computation
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Computer Methods and Programs in Biomedicine
Clustering and classifying informative attributes using rough set theory
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing, accessing, analysis, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, rough set theory is a new intelligent technique was used for the discovery of data dependencies, data reduction, approximate set classification, and rule induction from databases. In this paper, we present a rough set method for generating classification rules from a set of observed 360 samples of the breast cancer data. The attributes are selected, normalized and then the rough set dependency rules are generated directly from the real value attribute vector. Then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Experimental results from applying the rough set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known IDS classifier algorithm. The study showed that the theory of rough sets seems to be a useful tool for inductive learning and a valuable aid for building expert systems.