Fuzzy logic for business and industry
Fuzzy logic for business and industry
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Fuzzy Control
International Journal of Approximate Reasoning
On acquiring classification knowledge from noisy data based on rough set
Expert Systems with Applications: An International Journal
Analysis of classification in interval-valued information systems
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Method for classification in interval-valued information systems
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
A practical solution for the classification in interval-valued information systems
WSEAS Transactions on Systems and Control
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The problem of classification has been studied by many authors, and different methods have been developed. In this paper a combination of rough sets and fuzzy logic for classification is adopted. Rough set theory helps in minimizing the number of attributes that influence the selection. Using this technique, a group of rules can be extracted. When information is diffuse and the number of obtained values for each attribute is large, so is the number of rules. Even worst is hidden information in the data that makes the process complicated. Due to this fact, an interval of values is defined for each attribute, moving from the minimum to the maximum obtained values in the database. This is what is defined as interval-valued information systems. For discriminating between solutions that may give more than one possible object due to their similarity, a fuzzy logic discrimination is proposed, which is simple, and gives accuracy not less than other methods.