On estimating probabilities in tree pruning
EWSL-91 Proceedings of the European working session on learning on Machine learning
Rough computational methods for information systems
Artificial Intelligence
Uncertainly measures of rough set prediction
Artificial Intelligence
Rules in incomplete information systems
Information Sciences: an International Journal
Some mathematical structures for computational information
Information Sciences—Applications: An International Journal
Matrix computation for information systems
Information Sciences: an International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Learning Logical Definitions from Relations
Machine Learning
Fundamenta Informaticae
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Rough set theory is a kind of new tool to deal with knowledge, particularly when knowledge is imprecise, inconsistent and incomplete. In this paper, the main techniques of inductive machine learning are united to the knowledge reduction theory based on rough sets from the theoretical point of view. The Monk's problems introduced in the early of nineties are resolved again employing rough sets and their results are analyzed and compared with those of that time. As far as accuracy and conciseness are concerned, the learning algorithms based on rough sets have remarkable superiority.