Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
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
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
Classification with dynamic reducts and belief functions
Transactions on rough sets XIV
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In this paper, we deal with the problem of attribute selection from a sample of partially uncertain data. The uncertainty exists in decision attributes and is represented by the Transferable Belief Model (TBM), one interpretation of the belief function theory. To solve this problem, we propose dynamic reduct for attribute selection to extract more relevant and stable features for classification. The reduction of the uncertain decision table using this approach yields simplified and more significant belief decision rules for unseen objects.