Machine Learning
Boolean Reasoning for Decision Rules Generation
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Information Sciences: an International Journal
A rough set approach to multiple dataset analysis
Applied Soft Computing
Information Sciences: an International Journal
Transformation of bipolar fuzzy rough set models
Knowledge-Based Systems
A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice
Applied Soft Computing
Rough-Set-based timing characteristic analyses of distance protective relay
Applied Soft Computing
A general frame for intuitionistic fuzzy rough sets
Information Sciences: an International Journal
A New Version of the Rule Induction System LERS
Fundamenta Informaticae
Attribute selection based on a new conditional entropy for incomplete decision systems
Knowledge-Based Systems
Rough set approach to incomplete numerical data
Information Sciences: an International Journal
A method for extracting rules from spatial data based on rough fuzzy sets
Knowledge-Based Systems
Nullity-based matroid of rough sets and its application to attribute reduction
Information Sciences: an International Journal
Hi-index | 0.00 |
Decision rule mining is an important technique in many applications. In this paper, we propose a new rough set approach for rule induction based on a significance measure, called classification consistency rate. The approach implements the rule induction from the viewpoint of attribute rather than descriptor. The proposed algorithm is tested and compared with LEM2 algorithm on several real-life data sets added with different levels of inconsistent data. The results show that the proposed algorithm is effective in rule induction for inconsistent data.