2nd May, 2020

Sister Nivedita University

Question

Asked 19th Dec, 2014

In uncertainty models, there are models like Fuzzy model, Rough model and so on. But some papers use hybrid model naming as Fuzzy rough and Rough Fuzzy, so I like to know what is the differences between them?

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Fuzzy set works on **features** of the data whereas rough set works on **attribute set of the data (crisp)**. Therefore, the result of **rough-fuzzy hybridization set **is the approximation of fuzzy input into crisp approximation space. On the other hand, **fuzzy-rough set** refers the approximation of crisp set into fuzzy approximation space.

Dear friend

Greetings

Both are one and the same.

I have used fuzzy rough sets in my papers. you may look at them.

I hope this helps.

Best regards

Dr.indrajit Mandal

Dear Nouran Radwan,

As professor Ganapathy Ganesan has mentioned , Here is a **conceptual difference** between fuzzy rough set model and rough fuzzy set, we all know **rough se**t deals with indiscernible elements and **fuzzy set** with fuzzy sets and memberships,

it is better to use** rough fuzzy set modelling**, when we have specified knowledge concepts but approximated concepts are not so clear (ill definition), and when problem knowledge is fuzzy concepts and approximated concepts are specified we could use **fuzzy rough modelling**.

You could find **more** details in below links;

Sincerely,

or

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Rough-Fuzzy Set: Approximation of a fuzzy set in a crisp approximation space.

Fuzzy-Rough Set: Approximation of a crisp set in a fuzzy approximation space.

The Fuzzy set means that the elements in a set are not determinative or changing by the observer. The Rough set means that the boundary of set congtaining some elments are not determinative or changing by the observer. The Classical set are that both the elements in the set and the boundary of the set determinative.

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