International Journal of Man-Machine Studies
Comparison of rough-set and statistical methods in inductive learning
International Journal of Man-Machine Studies
Information Sciences: an International Journal
Rough set theory for the interval-valued fuzzy information systems
Information Sciences: an International Journal
Attributes Reduct and Optimal Decision Rules Acquisition in Fuzzy Objective Information Systems
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Knowledge Reduction in Incomplete and Fuzzy Objective Information Systems
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
Fuzzy Sets and Systems
A roughness measure for fuzzy sets
Information Sciences: an International Journal
Rough fuzzy set based scale space transforms and their use in image analysis
International Journal of Approximate Reasoning
A new rough sets model based on database systems
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Probabilistic rough sets characterized by fuzzy sets
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Hi-index | 0.00 |
Rough fuzzy sets are an effective mathematical analysis tool to deal with vagueness and uncertainty in the area of machine learning and decision analysis. Fuzzy information systems and fuzzy objective information systems exit in many applications and knowledge reduction in them can't be implemented by reduction methods in Pawlak information systems. Therefore, this paper provides a model for rule extraction in fuzzy information systems and fuzzy objective information systems. This approach uses inclusion degree to propose and represent a new and low computation complexity way for knowledge discovery and rough fuzzy concept classifier in fuzzy information systems and fuzzy objective information systems. Also, an illustration example in the construction sector is presented. This approach is a generalization of rough set model for fuzzy information system. Theory and method of attribute reduction under inclusion degree are suggested in this paper. This approach extends the classical rough set theory from complete information to fuzzy information system. This proposed model is useful for rule extraction in fuzzy information systems and fuzzy objective information systems to figure our knowledge reduction in fuzzy decision systems.