FSSC

2012 ◽  
Vol 2 (4) ◽  
pp. 29-46 ◽  
Author(s):  
Bana Handaga ◽  
Tutut Herawan ◽  
Mustafa Mat Deris

Introduced is a new algorithm for the classification of numerical data using the theory of fuzzy soft set, named Fuzzy Soft Set Classifier (FSSC). The algorithm uses the fuzzy approach in the pre-processing stage to obtain features, and similarity concept in the process of classification. It can be applied not only to binary-valued datasets, but also be able to classify the data that consists of real numbers. Comparison tests on seven datasets from UCI Machine Learning Repository have been carried out. It is shown that the proposed algorithm provides better accuracy and higher accuracy as compared to the baseline algorithm using soft set theory.

2018 ◽  
Vol 7 (3.34) ◽  
pp. 667
Author(s):  
K Selvakumari ◽  
S Lavanya

The Soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainity.This paper is devoted to the discussions of Neutrosophic fuzzy soft set. A new game modelis proposed and called Neutrosophicfuzzy soft game since it is based on Neutrosophic fuzzy soft set theory. We concentrate on discussing a class of two person zero-sum games with Neutrosophic fuzzy soft payoffs.The proposed scheme is illustrated by an example regarding the pure strategy problem.  


2020 ◽  
Vol 30 (1) ◽  
pp. 59-70
Author(s):  
Shehu Mohammed ◽  
Akbar Azam

The notion of soft set theory was initiated as a general mathematical tool for handling ambiguities. Decision making is viewed as a cognitive-based human activity for selecting the best alternative. In the present time, decision making techniques based on fuzzy soft sets have gained enormous attentions. On this development, this paper proposes a new algorithm for decision making in fuzzy soft set environment by hybridizing some existing techniques. The first novelty is the idea of absolute scores. The second concerns the concept of priority table in group decision making problems. The advantages of our approach herein are stronger power of objects discrimination and a well-determined inference.


2014 ◽  
Vol 85 (7) ◽  
pp. 27-31 ◽  
Author(s):  
Krishna Gogoi ◽  
Alock Kr. Dutta ◽  
Chandra Chutia
Keyword(s):  
Soft Set ◽  

2009 ◽  
Vol 2009 ◽  
pp. 1-6 ◽  
Author(s):  
B. Ahmad ◽  
Athar Kharal

We further contribute to the properties of fuzzy soft sets as defined and studied in the work of Maji et al. ( 2001), Roy and Maji (2007), and Yang et al. (2007) and support them with examples and counterexamples. We improve Proposition 3.3 by Maji et al., (2001). Finally we define arbitrary fuzzy soft union and fuzzy soft intersection and prove DeMorgan Inclusions and DeMorgan Laws in Fuzzy Soft Set Theory.


2020 ◽  
Vol 38 (2) ◽  
pp. 1789-1797 ◽  
Author(s):  
Hashem Bordbar ◽  
Seok-Zun Song ◽  
Mohammad Rahim Bordbar ◽  
Young Bae Jun ◽  
Keyword(s):  
Soft Set ◽  

2020 ◽  
Vol 8 (4) ◽  
pp. 1661-1664
Author(s):  
Snekaa B. ◽  
R. Sophia Porchelvi

2020 ◽  
Vol 8 (4) ◽  
pp. 1577-1579
Author(s):  
Snekaa B. ◽  
Dorathy C. ◽  
R. Sophia Porchelvi

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