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It is known that dependency is an important feature evaluation measure based on rough set theory.
To resolve these problems, we propose a HRNeuro fuzzy system in this paper based on rough set theory and holoentropy function.
In recent years several decision models based on Rough Set Theory (e.g. three-way decision rules) and Fuzzy Cognitive Maps have been introduced for addressing such problems.
Since the design rules are difficult to extract during the design process, the reduction algorithm based on rough set theory is applied to data mining.
This paper presents a hybrid decision support system based on Rough Set Theory (RST) and Bat optimization Algorithm (BA) called RST-BatMiner.
In this paper, based on rough set theory we address the problem of feature selection for cost-sensitive data with missing values.
Similar(40)
Some form features were deleted by using an attribute reduction algorithm based on Rough Sets Theory (RS T.
Using feature selection algorithm based on rough sets theory, six main indicators were identified as the most effective factors.
Two forms of neural computation are considered: fuzzy neural computation based on fuzzy sets and rough neural computation based on rough sets.
As a necessary step for knowledge discovery based on rough sets, the fuzzy rough approximations of fuzzy ISTU need to be updated efficiently under dynamic data environment.
The proposed analytical model is based on Rough Sets theory and identifies decision rules which give statistically significant association between product pedigree and repair actions.
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