Variable precision rough set model
Journal of Computer and System Sciences
Communications of the ACM
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Rough set algorithms in classification problem
Rough set methods and applications
Statistical techniques for rough set data analysis
Rough set methods and applications
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-Neuro-Computing: Techniques for Computing with Words
Rough-Neuro-Computing: Techniques for Computing with Words
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Discovery of Rules about Compilations - A Rough Set Approach in Medical Knowledge Discovery
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Journal of the American Society for Information Science and 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
Intelligence techniques for prostate ultrasound image analysis
International Journal of Hybrid Intelligent Systems
Going-concern prediction using hybrid random forests and rough set approach
Information Sciences: an International Journal
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The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7-13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, 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. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminant analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.