C4.5: programs for machine learning
C4.5: programs for machine learning
Communications of the ACM
A new version of the rule induction system LERS
Fundamenta Informaticae
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough set methods and applications: new developments in knowledge discovery in information systems
Rough set algorithms in classification problem
Rough set methods and applications
Artificial Intelligence
Rough-Neuro-Computing: Techniques for Computing with Words
Rough-Neuro-Computing: Techniques for Computing with Words
Machine Learning
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Knowledge and Uncertainty: A Rough Set Approach
Proceedings of the SOFTEKS Workshop on Incompleteness and Uncertainty in Information Systems
The Application of Support Diagnose in Mitochondrial Encephalomyopathies
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Applying rough set theory to multi stage medical diagnosing
Fundamenta Informaticae
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Attribute selection and rule generation techniques for medical diagnosis systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Rough sets approach to medical diagnosis system
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Introducing a rule importance measure
Transactions on Rough Sets V
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Success of machine learning algorithms is usually dependent on a quality of a dataset they operate on. For datasets containing noisy, inadequate or irrelevant information these algorithms may produce less accurate results. Therefore a common pre-processing step in data mining domain is a selection of highly predictive attributes. In this case study we select subsets of attributes from medical data using filter feature selection algorithms. To validate the algorithms we induce decision rules from the selected subsets of attributes and compare classification accuracy on both training and test datasets. Additionally medical relevance of the selected attributes is checked with help of domain experts.