Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Artificial Intelligence
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Combining belief functions when evidence conflicts
Decision Support Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Probabilistic Rough Induction: The GDT-RS Methodology and Algorithms
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Pruning belief decision tree methods in averaging and conjunctive approaches
International Journal of Approximate Reasoning
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Dynamic Reduct from Partially Uncertain Data Using Rough Sets
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
A comparison of dynamic and static belief rough set classifier
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Rule discovery process based on rough sets under the belief function framework
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Assessing sensor reliability for multisensor data fusion within the transferable belief model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The modified Dempster-Shafer approach to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, we propose two approaches of classification namely, Dynamic Belief Rough Set Classifier (D-BRSC) and Dynamic Belief Rough Set Classifier based on Generalization Distribution Table (D-BRSC-GDT). Both the classifiers are induced from uncertain data to generate classification rules. The uncertainty appears only in decision attribute values and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. D-BRSC only uses the basic concepts of Rough Sets (RS). However, D-BRSC-GDT is based on GDT-RS which is a hybridization of Generalization Distribution Table (GDT) and Rough Sets (RS). The feature selection step relative to the construction of the two classifiers uses the approach of dynamic reduct which extracts more relevant and stable features. The reduction of uncertain and noisy decision table using dynamic approach generates more significant decision rules for the classification of unseen objects. To prove that, we carry experimentations on real databases according to three evaluation criteria including the classification accuracy. We also compare the results of D-BRSC and D-BRSC-GDT with those obtained from Static Belief Rough Set Classifier (S-BRSC) and Static Belief Rough Set Classifier based on Generalization Distribution Table (S-BRSC-GDT). To further evaluate our rough sets based classification systems, we compare our results with those obtained from the Belief Decision Tree (BDT).