Knowledge discovery in databases: an attribute-oriented rough set approach
Knowledge discovery in databases: an attribute-oriented rough set approach
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Reduction algorithms based on discernibility matrix: the ordered attributes method
Journal of Computer Science and Technology
Expert Systems and Applied Artificial Intelligence
Expert Systems and Applied Artificial Intelligence
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
A novel self-optimizing approach for knowledge acquisition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Prediction of MHC II-binding peptides using rough set-based rule sets ensemble
Applied Intelligence
Control approach to rough set reduction
Computers & Mathematics with Applications
A New Rough Sets Decision Method Based on PCA and Ordinal Regression
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Knowledge structure, knowledge granulation and knowledge distance in a knowledge base
International Journal of Approximate Reasoning
Set-valued ordered information systems
Information Sciences: an International Journal
IQuickReduct: An Improvement to Quick Reduct Algorithm
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
MGRS: A multi-granulation rough set
Information Sciences: an International Journal
An attribute reduct algorithm based on clustering
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
A global unsupervised data discretization algorithm based on collective correlation coefficient
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Attribute reduction: A dimension incremental strategy
Knowledge-Based Systems
Multigranulation decision-theoretic rough sets
International Journal of Approximate Reasoning
Pessimistic rough set based decisions: A multigranulation fusion strategy
Information Sciences: an International Journal
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Rough set (RS) theory gives an approach to knowledge acquisition. Most RS-based reduction algorithms are to find minimal attribute reducts with heuristic knowledge derived only from the dependencies between condition attributes and decision attributes. The rules based on minimal attribute reducts are too simple to represent expert knowledge well. To acquire rules with stronger generalization capabilities, we advocate a knowledge acquisition approach based on RS and Principal Component Analysis (KA-RSPCA). KA-RSPCA uses a collective correlation coefficient as heuristic knowledge to assist attribute reduction and attribute value reduction. The coefficient is a PCA-based quantitative index to measure every condition attribute's contributions to the state space constructed by the whole of the condition attributes in a decision table.For two test data sets in comparison with other algorithms, KA-RSPCA algorithm shows a reduction in error rate with only a slight increase in the number of rules used.