Generalised fuzzy soft sets

Majumdar, Parthasarathi; Samanta, Sourav

doi:10.1016/j.camwa.2009.12.006

Cited by 360 publications

(177 citation statements)

References 7 publications

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“…Qin and Kong (2010) introduced the concept of soft equality and derived some related properties. Several extension models including vague soft sets (Xu et al, 2010), fuzzy soft sets (Maji et al, 2001a;Majumdar and Samanta, 2010), intuitionistic fuzzy soft sets (Maji, 2009;Maji et al, 2001b;2004), interval-valued fuzzy soft sets (Yang et al, 2009) and interval-valued intuitionistic fuzzy soft sets (Jiang et al, 2010) are proposed in succession. It could be shown that soft set theory is closely associated with rough sets (Pei and Miao, 2005;Herawan and Mat Deris, 2009;Feng et al, 2009;Feng, 2009).…”

Section: Introductionmentioning

confidence: 99%

DFIS: A novel data filling approach for an incomplete soft set

Qin¹,

Ma²,

Herawan³

et al. 2012

International Journal of Applied Mathematics and Computer Science

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The research on incomplete soft sets is an integral part of the research on soft sets and has been initiated recently. However, the existing approach for dealing with incomplete soft sets is only applicable to decision making and has low forecasting accuracy. In order to solve these problems, in this paper we propose a novel data filling approach for incomplete soft sets. The missing data are filled in terms of the association degree between the parameters when a stronger association exists between the parameters or in terms of the distribution of other available objects when no stronger association exists between the parameters. Data filling converts an incomplete soft set into a complete soft set, which makes the soft set applicable not only to decision making but also to other areas. The comparison results elaborated between the two approaches through UCI benchmark datasets illustrate that our approach outperforms the existing one with respect to the forecasting accuracy.

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Section: Introductionmentioning

confidence: 99%

DFIS: A novel data filling approach for an incomplete soft set

Qin¹,

Ma²,

Herawan³

et al. 2012

International Journal of Applied Mathematics and Computer Science

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“…In recent years, soft set theory has drawn considerable attention [4][5][6][7][8]. By integrating soft set theory with other classical mathematical models, a lot of extensions of soft set model have also been made [9][10][11], fuzzy soft sets, generalized fuzzy soft sets [12], interval-valued fuzzy soft sets, vague soft sets [13], and intuitionistic fuzzy soft sets are all the models of soft set. At the same time, some approaches of decision making to these extended fuzzy soft sets have also been rendered.…”

Section: Introductionmentioning

confidence: 99%

The Application of Intuitionistic Fuzzy Soft Opeations to Medical Diagnosis

Zhang¹,

Gao²,

Gao³

2015

Proceedings of the 2015 International Conference on Education, Management, Information and Medicine

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Abstract. Molodtsov first proposed the soft set theory, which can be utilized as a general mathematical tool for dealing with fuzzy and uncertain information. As a generalization of soft set, intuitionistic fuzzy soft set is another soft set structure, it is a combination of an interval-valued fuzzy set and a soft set. The aim of this paper is to explore the decision making based on interval-valued fuzzy soft sets, the operations are carried out to integrate the medical information and expert information together, the optimcal choice is calculated to choose the disease which the patient most likely to suffer, a numberical example shows that the approach is reasonable..

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“…Then, many interesting results of soft set theory have been studied on fuzzy soft sets [8,12,23], on intuitionistic fuzzy soft set theory [14,25], on possibility fuzzy soft set [7], on generalized fuzzy soft sets [26,29], on generalized intuitionistic fuzzy soft [6], on interval-valued intuitionistic fuzzy soft sets [20], on intuitionistic neutrosophic soft set [9], on generalized neutrosophic soft set [10], on fuzzy parameterized soft set theory [17,18], on fuzzy parameterized fuzzy soft set theory [13], on intuitionistic fuzzy parameterized soft set theory [15], on IFP−fuzzy soft set theory [16],on neutrosophic soft set [24].interval-valued neutrosophic soft set [11,19].…”

Section: Introductionmentioning

confidence: 99%