A Software Engineering Cognitive Knowledge Discovery Framework
ICCI '02 Proceedings of the 1st IEEE International Conference on Cognitive Informatics
Rough Set Data Mining of Diabetes Data
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Knowledge-Based Education Tool to Improve Quality in Diabetes Care
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
Melanoma Prediction Using Data Mining System LERS
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
An introduction to variable and feature selection
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules
Information Sciences: an International Journal - Special issue: Soft computing data mining
Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
Information Sciences: an International Journal - Special issue: Medical expert systems
Concept Formation and Learning: A Cognitive Informatics Perspective
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Combined SVM-Based Feature Selection and Classification
Machine Learning
Diabetic e-Management System (DEMS)
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
A survey of Knowledge Discovery and Data Mining process models
The Knowledge Engineering Review
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Feature selection and classification model construction on type 2 diabetic patients' data
Artificial Intelligence in Medicine
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
On acquiring classification knowledge from noisy data based on rough set
Expert Systems with Applications: An International Journal
Bayesian decision theory for dominance-based rough set approach
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Multi-knowledge extraction and application
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Highly scalable and robust rule learner: performance evaluation and comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data mining a diabetic data warehouse
Artificial Intelligence in Medicine
Intelligent analysis of clinical time series: an application in the diabetes mellitus domain
Artificial Intelligence in Medicine
Adaptive Study Design Through Semantic Association Rule Analysis
International Journal of Software Science and Computational Intelligence
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From the perspective of cognitive informatics, cognition can be viewed as the acquisition of knowledge. In real-world applications, information systems usually contain some degree of noisy data. A new model proposed to deal with the hybrid-feature selection problem combines the neighbourhood approximation and variable precision rough set models. Then rule induction algorithm can learn from selected features in order to reduce the complexity of rule sets. Through proposed integration, the knowledge acquisition process becomes insensitive to the dimensionality of data with a pre-defined tolerance degree of noise and uncertainty for misclassification. When the authors apply the method to a Chinese diabetic diagnosis problem, the hybrid-attribute reduction method selected only five attributes from totally thirty-four measurements. Rule learner produced eight rules with average two attributes in the left part of an IF-THEN rule form, which is a manageable set of rules. The demonstrated experiment shows that the present approach is effective in handling real-world problems.