Interval Valued Hesitant Fuzzy Soft Sets and Its Application in Stock Market Analysis
01 Jan 2017-pp 755-764
TL;DR: This paper extends the concept of interval valued fuzzy soft set by introducing interval valued hesitant fuzzy soft sets (IVHFSS) through the membership function approach introduced by Tripathy et al. in 2015.
Abstract: Molodtsov introduced soft set theory in 1999 to handle uncertainty. It has been found that hybrid models are more useful than that of individual components. Yang et al. introduced the concept of interval valued fuzzy soft set (IVFSS) by combining the interval valued fuzzy sets (IVFS) and soft set model. In this paper we extend it by introducing interval valued hesitant fuzzy soft sets (IVHFSS) through the membership function approach introduced by Tripathy et al. in 2015. To illustrate the application of the new model, we provide a decision making algorithm and use it in stock market analysis,
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01 Apr 2018
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5 citations
01 Jul 2018
TL;DR: The authors propose a group decision making algorithm using IVIFSS, which generalises many of the earlier algorithms and compute its complexity and establish the computation experimentally with graphical illustrations.
Abstract: This article describes how the lack of adequate parametrization in some of the earlier uncertainty based models like fuzzy sets, rough sets motivated Molodtsov to introduce a new model in soft set. A suitable combination of individual models leads to hybrid models, which are more efficient than their individual components. So, the authors find the introduction of many hybrid models of soft sets, like the fuzzy soft set (FSS), intuitionistic fuzzy soft sets (IFSS), interval valued fuzzy soft set (IVFSS) and the interval valued intuitionistic fuzzy soft set (IVIFSS). Following the characteristic function approach to define soft sets introduced by Tripathy et al., they re-define IVIFSS in this article. One of the most attractive applications of soft set theory and its hybrid models has been decision making in the form of individual decision making or group decision making. Here, the authors propose a group decision making algorithm using IVIFSS, which generalises many of our earlier algorithms. They compute its complexity and establish the computation experimentally with graphical illustrations.
5 citations
01 Jan 2018
TL;DR: This chapter proposes an algorithm in this direction by using a hybrid model formed by combining the two models of soft set and the intuitionistic fuzzy set, following the characteristic function approach used by Tripathy et al. for the purpose.
Abstract: Decision making has become a common feature in day-to-day activities. Uncertainty-based models are more efficient in handling such problems. In this chapter, we propose an algorithm in this direction by using a hybrid model formed by combining the two models of soft set (SS) and the intuitionistic fuzzy set (IFS). We follow the characteristic function approach used by Tripathy et al. for the purpose. The illustrative real-life example shows the efficiency of our algorithm over other such models.
4 citations
01 Jan 2018
TL;DR: This paper redefine interval-valued intuitionistic hesitant fuzzy soft sets (IVIHFSS) and also proposes a decision-making technique which extends some of the recently developed algorithms.
Abstract: There are several models of uncertainty found in the literature like fuzzy set (FS), rough set, intuitionistic fuzzy set, soft set, and hesitant fuzzy set. Also, several hybrid models have come up as a combination of these models and have been found to be more useful than the individual models. In everyday life, we make many decisions. Making efficient decisions under uncertainty needs better techniques. Many such techniques have been developed in the recent past. These techniques involve soft sets (SS) and intuitionistic fuzzy sets. It is well known that intuitionistic hesitant fuzzy sets are more general than intuitionistic fuzzy sets. In this paper, we redefine interval-valued intuitionistic hesitant fuzzy soft sets (IVIHFSS) and also propose a decision-making technique which extends some of the recently developed algorithms. We also provide an application from real-life situations which illustrates the working of the algorithm and its efficiency over the other algorithms.
3 citations
01 Jan 2022
TL;DR: In this article , the authors provide many insights related to nanotechnology in health care, nanoinformatics, and decision-making methodologies involving in it, which need to be addressed quickly to aid future research.
Abstract: Nanotechnology has become one of the most sought after area of research currently. Increase in research in a new research field leads to mostly unorganized, heterogeneous, and huge volume of data. To take benefit from that huge amount of data, the necessity of informatics is paramount. To build nanoinformatics repositories, role of different decision-making methods comes to fore. Among several associated interdisciplinary field of research, healthcare sector is the most important one. So, nanoinformatics is the one of the important areas which need to be addressed quickly to aid future research. This paper provides many insights related to nanotechnology in health care, nanoinformatics, and decision-making methodologies involving in it.
References
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TL;DR: The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the the theory, and to discuss some problems of the future.
Abstract: The soft set theory offers a general mathematical tool for dealing with uncertain, fuzzy, not clearly defined objects. The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the theory, and to discuss some problems of the future.
3,759 citations
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TL;DR: The authors define equality of two soft sets, subset and super set of a soft set, complement of asoft set, null soft set and absolute soft set with examples and De Morgan's laws and a number of results are verified in soft set theory.
Abstract: In this paper, the authors study the theory of soft sets initiated by Molodtsov. The authors define equality of two soft sets, subset and super set of a soft set, complement of a soft set, null soft set, and absolute soft set with examples. Soft binary operations like AND, OR and also the operations of union, intersection are defined. De Morgan's laws and a number of results are verified in soft set theory.
2,114 citations
TL;DR: In this article, the theory of soft sets was applied to solve a decision-making problem using rough mathematics, and the results showed that soft sets can be used to solve decision making problems.
Abstract: In this paper, we apply the theory of soft sets to solve a decision making problem using rough mathematics.
1,491 citations
02 Oct 2009
TL;DR: The hesitant fuzzy sets as mentioned in this paper are a generalization of fuzzy sets where the membership is an interval, instead of being a single value, and they have been used in decision making.
Abstract: Intuitionistic Fuzzy Sets (IFS) are a generalization of fuzzy sets where the membership is an interval. That is, membership, instead of being a single value, is an interval. A large number of operations have been defined for this type of fuzzy sets, and several applications have been developed in the last years. In this paper we describe hesitant fuzzy sets. They are another generalization of fuzzy sets. Although similar in intention to IFS, some basic differences on their interpretation and on their operators exist. In this paper we review their definition, the main results and we present an extension principle, which permits to generalize existing operations on fuzzy sets to this new type of fuzzy sets. We also discuss their use in decision making.
1,009 citations