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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.
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.
Two forms of neural computation are considered: fuzzy neural computation based on fuzzy sets and rough neural computation based on rough sets.
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.
This paper presents a novel feature selection technique based on Rough Sets (RS) and few interesting properties of Hypergraph (RSHGT), such as minimal transversal and vertex linearity for the identification of the optimal feature subset.
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It is known that dependency is an important feature evaluation measure based on rough set theory.
It showed the area difference based on rough set was smaller by comparing the glacier areas of the rough set method and MLC with visual interpreter, respectively.
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.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com