Sentence examples for fuzzy ranking from inspiring English sources

The effect of uncertainties is also taken into account through fuzzy ranking of the sectors.

The information of the local sample spread is tracked by the fuzzy ranking through its adaptive fuzzy sample similarity function.

As for the choice of FN, DT, and fuzzy ranking, the authors discovered them as good candidates for feature selection in their comprehensive literature search.

In this paper, we propose a novel intelligent framework that integrates fuzzy ranking (FR) and multilayer perceptron (MLP) neural networks for reservoir characterization.

An extensive search in the literature revealed only three algorithms that have been used for feature selection: functional networks, decision trees, and fuzzy information entropy (FIE) (also known as fuzzy ranking).

The hybrid models utilized the non-linear feature selection capabilities of Functional Networks (FN), Decision Trees (DT) and Fuzzy Ranking (FR) algorithms with the excellent functional approximation capability of Support Vector Machines (SVM).

The proposed hybrid models include FN-SVM, DT-SVM, fuzzy ranking-SVM, FN-T2FLS, and FN-ELM.

For the validation of the proposed ranking method, the following reasonable axioms that Wang and Kerre [13] have proposed for fuzzy numbers' ranking are considered.

It is observed that an alternative RFID service provider that does not meet the defined Functional Requirements (FRs) is directly eliminated by Fuzzy AD, whereas fuzzy TOPSIS ranks it as an eligible one.

The main difficulty in ranking fuzzy numbers is that fuzzy numbers do not always yield a totally ordered set as real numbers do (Yoon 1996).

Comparing or ranking fuzzy numbers plays a crucial role in fuzzy decision problems, e.g., the concept of optimal or best choice is based on ranking.