C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial Intelligence
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Machine Learning - Special issue on learning with probabilistic representations
Combining belief functions when evidence conflicts
Decision Support Systems
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Applying Knowledge Discovery to Predict Water-Supply Consumption
IEEE Expert: Intelligent Systems and Their Applications
Probabilistic Rough Induction: The GDT-RS Methodology and Algorithms
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information 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
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
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables
International Journal of Approximate Reasoning
A rough set approach to data with missing attribute values
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Clustering approach using belief function theory
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
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
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An axiomatic characterization of probabilistic rough sets
International Journal of Approximate Reasoning
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In this paper, we present two classification approaches based on Rough Sets (RS) that are able to learn decision rules from uncertain data. We assume that the uncertainty exists only in the decision attribute values of the Decision Table (DT) and is represented by the belief functions. The first technique, named Belief Rough Set Classifier (BRSC), is based only on the basic concepts of the Rough Sets (RS). The second, called Belief Rough Set Classifier, is more sophisticated. It is based on Generalization Distribution Table (BRSC-GDT), which is a hybridization of the Generalization Distribution Table and the Rough Sets (GDT-RS). The two classifiers aim at simplifying the Uncertain Decision Table (UDT) in order to generate significant decision rules for classification process. Furthermore, to improve the time complexity of the construction procedure of the two classifiers, we apply a heuristic method of attribute selection based on rough sets. To evaluate the performance of each classification approach, we carry experiments on a number of standard real-world databases by artificially introducing uncertainty in the decision attribute values. In addition, we test our classifiers on a naturally uncertain web usage database. We compare our belief rough set classifiers with traditional classification methods only for the certain case. Besides, we compare the results relative to the uncertain case with those given by another similar classifier, called the Belief Decision Tree (BDT), which also deals with uncertain decision attribute values.