Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
Discovery through rough set theory
Communications of the ACM
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Expert Systems with Applications: An International Journal
1-vs-others rough decision forest
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
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The proposed hybridized rough set framework is composed of traditional Rough Set (RS) approach and classical Decision Tree (DT) induction algorithm. RS helps to identify dominant attributes and DT algorithm results in simpler and generalized classifier. The implementation of the Hybridized Rough Set Framework is presented as the RDT algorithm. GA heuristics are used to generalize the RDT algorithm further. Experimental results obtained on applying the hybridized rough set framework and the related base algorithms on datasets belonging to the three categories are presented in this paper. Accuracy, complexity, number of rules and number of attributes in the induced classifier assess the performance of the candidate algorithms. The results indicate that the proposed framework is an effective model for classification.