Comparison of rough-set and statistical methods in inductive learning
International Journal of Man-Machine Studies
Rough sets: a new approach to vagueness
Fuzzy logic for the management of uncertainty
C4.5: programs for machine learning
C4.5: programs for machine learning
Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Machine Learning
A fuzzy-rough case-based learning approach for intelligent die design
International Journal of Computer Applications in Technology
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The efficient use of critical machines or equipment in a manufacturing system requires reliable information about their current operating conditions. This information is often used as a basis for machine condition monitoring and fault diagnosis—which essentially is an endeavor of knowledge extraction. Rough set theory provides a novel way to knowledge acquisition, especially when dealing with vagueness and uncertainty. It focuses on the discovery of patterns in incomplete and/or inconsistent data. However, rough set theory requires the data analyzed to be in discrete manner. This paper proposes a novel approach to the treatment of continuous-valued attributes in multi-concept classification for mechanical diagnosis using rough set theory. Based on the proposed approach, a prototype system called RClass-Plus has been developed. RClass-Plus is validated using a case study on mechanical fault diagnosis. Details of the validation are described.