Rough set approach to incomplete information systems
Information Sciences: an International Journal
On Databases with Incomplete Information
Journal of the ACM (JACM)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Accuracy and Coverage in Rough Set Rule Induction
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Fundamenta Informaticae
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
Contents modelling of Neo-Sumerian Ur III economic text corpus
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
An experimental comparison of three rough set approaches to missing attribute values
Transactions on rough sets VI
Transactions on rough sets VIII
A rough set approach to data with missing attribute values
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Model selection and assessment for classification using validation
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Approximation spaces and information granulation
Transactions on Rough Sets III
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In this paper we propose the hybridization of the rough set concepts and statistical learning theory. We introduce new estimators for rule accuracy and coverage, which base on the assumptions of the statistical learning theory. These estimators allow us to select rules describing statistically significant dependencies in data. Then we construct classifier which uses these estimators for rule induction. In order to make our solution applicable for information systems with missing values and multiple valued attributes, we propose axiomatic representation of information systems and we redefine the indiscernibility relation as a relation on objects characterized by axioms. Finally, we test our classifier on benchmark datasets.