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
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
An empirical comparison of supervised machine learning techniques in bioinformatics
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Applying rough set theory to multi stage medical diagnosing
Fundamenta Informaticae
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
From an Information System to a Decision Support System
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
From Information System to Decision Support System
Transactions on Rough Sets IX
Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Visualization of Rough Set Decision Rules for Medical Diagnosis Systems
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Data preparation for data mining in medical data sets
Transactions on rough sets VI
Selection of important attributes for medical diagnosis systems
Transactions on rough sets VII
Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
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Success of many learning schemes is based on selection of a small set of highly predictive attributes. The inclusion of irrelevant, redundant and noisy attributes in the process model can result in poor predictive accuracy and increased computation. This paper shows that the accuracy of classification can be improved by selecting subsets of strong attributes. Attribute selection is performed by using the Wrapper method with several classification learners. The processed data are classified by diverse learning schemes and generated “if-then” rules are supervised by domain experts.