Relevant bibliographies by topics / Rough Intuitionistic Fuzzy Set

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Rough Intuitionistic Fuzzy Set'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Rough Intuitionistic Fuzzy Set"

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Zhang, Haidong, Lan Shu, and Shilong Liao. "Intuitionistic Fuzzy Soft Rough Set and Its Application in Decision Making." Abstract and Applied Analysis 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/287314.

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The soft set theory, originally proposed by Molodtsov, can be used as a general mathematical tool for dealing with uncertainty. In this paper, we present concepts of soft rough intuitionistic fuzzy sets and intuitionistic fuzzy soft rough sets, and investigate some properties of soft rough intuitionistic fuzzy sets and intuitionistic fuzzy soft rough sets in detail. Furthermore, classical representations of intuitionistic fuzzy soft rough approximation operators are presented. Finally, we develop an approach to intuitionistic fuzzy soft rough sets based on decision making and a numerical examp
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Attaullah, Sultan Alyobi, Mohammed Alharthi, and Yasser Alrashedi. "Selection of an Appropriate Global Partner for Companies Using the Innovative Extension of the TOPSIS Method with Intuitionistic Hesitant Fuzzy Rough Information." Axioms 13, no. 9 (2024): 610. http://dx.doi.org/10.3390/axioms13090610.

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In this research, we introduce the intuitionistic hesitant fuzzy rough set by integrating the notions of an intuitionistic hesitant fuzzy set and rough set and present some intuitionistic hesitant fuzzy rough set theoretical operations. We compile a list of aggregation operators based on the intuitionistic hesitant fuzzy rough set, including the intuitionistic hesitant fuzzy rough Dombi weighted arithmetic averaging aggregation operator, the intuitionistic hesitant fuzzy rough Dombi ordered weighted arithmetic averaging aggregation operator, and the intuitionistic hesitant fuzzy rough Dombi hy
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Wang, Hong, and Hong Li. "Uncertainty Measure for Multisource Intuitionistic Fuzzy Information System." Complexity 2022 (April 7, 2022): 1–21. http://dx.doi.org/10.1155/2022/3605881.

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Multisource information systems and multigranulation intuitionistic fuzzy rough sets are important extended types of Pawlak’s classical rough set model. Multigranulation intuitionistic fuzzy rough sets have been investigated in depth in recent years. However, few studies have considered this combination of multisource information systems and intuitionistic fuzzy rough sets. In this paper, we give the uncertainty measure for multisource intuitionistic fuzzy information system. Against the background of multisource intuitionistic fuzzy information system, each information source is regarded as a
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Chuanchao, Zhang. "Generalized dynamic attribute reduction based on similarity relation of intuitionistic fuzzy rough set." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7107–22. http://dx.doi.org/10.3233/jifs-200347.

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In view of the characteristics with big data, high feature dimension, and dynamic for a large-scale intuitionistic fuzzy information systems, this paper integrates intuitionistic fuzzy rough sets and generalized dynamic sampling theory, proposes a generalized attribute reduction algorithm based on similarity relation of intuitionistic fuzzy rough sets and dynamic reduction. It uses dynamic reduction sampling theory to divide a big data set into small data sets and relative positive domain cardinality instead of dependency degree as decision-making condition, and obtains reduction attributes of
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Ahmmad, Jabbar, Tahir Mahmood, Nayyar Mehmood, Khamika Urawong, and Ronnason Chinram. "Intuitionistic Fuzzy Rough Aczel-Alsina Average Aggregation Operators and Their Applications in Medical Diagnoses." Symmetry 14, no. 12 (2022): 2537. http://dx.doi.org/10.3390/sym14122537.

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Managing ambiguous and asymmetric types of information is a very challenging task under the consideration of classical data. Furthermore, Aczel-Alsina aggregation operators are the new developments in fuzzy sets theory. However, when decision-makers need to use these structures in fuzzy rough structures, these operators fail to deal with such types of values, as fuzzy rough structures use lower and upper approximation spaces. Thus, an encasement of an intuitionistic fuzzy set has a chance of data loss, whereas an intuitionistic fuzzy rough set can resolve the problem of data loss. Motivated by
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Wang, Jingqian, and Xiaohong Zhang. "Two Types of Intuitionistic Fuzzy Covering Rough Sets and an Application to Multiple Criteria Group Decision Making." Symmetry 10, no. 10 (2018): 462. http://dx.doi.org/10.3390/sym10100462.

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Intuitionistic fuzzy rough sets are constructed by combining intuitionistic fuzzy sets with rough sets. Recently, Huang et al. proposed the definition of an intuitionistic fuzzy (IF) β -covering and an IF covering rough set model. In this paper, some properties of IF β -covering approximation spaces and the IF covering rough set model are investigated further. Moreover, we present a novel methodology to the problem of multiple criteria group decision making. Firstly, some new notions and properties of IF β -covering approximation spaces are proposed. Secondly, we study the characterizations of
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DEMİRCİ, MUSTAFA. "GENUINE SETS, VARIOUS KINDS OF FUZZY SETS AND FUZZY ROUGH SETS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 04 (2003): 467–94. http://dx.doi.org/10.1142/s0218488503002193.

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In this paper, deriving the type-m fuzzy sets, intuitionistic fuzzy sets, Φ-fuzzy sets, rough sets, fuzzy rough sets and rough fuzzy sets as particular genuine sets, and establishing their connections with genuine sets, it is demonstrated that the theory of genuine sets provides a powerful tool to model various different kinds of uncertainty in a mathematical way. Furthermore, it is also shown that the genuine set theoretic descriptions of type-m fuzzy sets, intuitionistic fuzzy sets and fuzzy rough sets point out new features of these set notations, originated from the peculiar characteristic
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Alsager, Kholood M., and Sheza M. El-Deeb. "Rough and T-Rough Sets Arising from Intuitionistic Fuzzy Ideals in BCK-Algebras." Mathematics 12, no. 18 (2024): 2925. http://dx.doi.org/10.3390/math12182925.

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This paper presents the novel concept of rough intuitionistic fuzzy ideals within the realm of BCK-algebras and investigates their fundamental properties. Furthermore, we introduce a set-valued homomorphism over a BCK-algebra, laying the foundation for the establishment of T-rough intuitionistic fuzzy ideals. The characterization of these innovative ideals is accomplished by employing the (α,β)-cut of intuitionistic fuzzy sets in the context of BCK-algebras.
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Huang, Bing. "Degree Dominance Interval Relation-Based RSM in Intuitionistic Fuzzy Decision Systems." Applied Mechanics and Materials 48-49 (February 2011): 357–61. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.357.

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By introducing a degree dominance relation to dominance interval intuitionistic fuzzy decision systems, we establish a degree dominance interval rough set model (RSM), which is mainly based on replacing the indiscernibility relation in classical rough set theory with the degree dominance interval relation. To simplify knowledge representation and extract some nontrivial simpler degree dominance interval intuitionistic fuzzy decision rules, we propose two attribute reductions of the degree dominance interval intuitionistic fuzzy decision systems that eliminate the redundant condition attributes
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Zishan Anwar, Muhammad, Shahida Bashir, and Muhammad Shabir. "An Efficient Model for the Approximation of Intuitionistic Fuzzy Sets in terms of Soft Relations with Applications in Decision Making." Mathematical Problems in Engineering 2021 (October 22, 2021): 1–19. http://dx.doi.org/10.1155/2021/6238481.

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The basic notions in rough set theory are lower and upper approximation operators defined by a fixed binary relation. This paper proposes an intuitionistic fuzzy rough set (IFRS) model which is a combination of intuitionistic fuzzy set (IFS) and rough set. We approximate an IFS by using soft binary relations instead of fixed binary relations. By using this technique, we get two pairs of intuitionistic fuzzy (IF) soft sets, called the upper approximation and lower approximation with respect to foresets and aftersets. Properties of newly defined rough set model (IFRS) are studied. Similarity rel
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Dissertations / Theses on the topic "Rough Intuitionistic Fuzzy Set"

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Zettervall, Hang. "Fuzzy and Rough Set Theory in Treatment of Elderly Gastric Cancer Patients." Licentiate thesis, Karlskrona : Blekinge Institute of Technology, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00489.

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Fuzzy set theory was presented for the first time by Professor Lotfi A. Zadeh from Berkeley University in 1965. In conventional binary logic a statement can be true or false, and there is no place for even a little uncertainty in this judgment. An element either belongs to a set or does not. We call these kinds of sets crisp sets. In practice we often experience those real situations that are represented by crisp sets as impossible to describe accurately. A two-valued logic assumes that precise symbols must be employed, and it is therefore not applicable to the real existence. If the informati
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Lee, Chang Su. "A framework of adaptive T-S type rough-fuzzy inference systems (ARFIS)." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0192.

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[Truncated abstract] Fuzzy inference systems (FIS) are information processing systems using fuzzy logic mechanism to represent the human reasoning process and to make decisions based on uncertain, imprecise environments in our daily lives. Since the introduction of fuzzy set theory, fuzzy inference systems have been widely used mainly for system modeling, industrial plant control for a variety of practical applications, and also other decisionmaking purposes; advanced data analysis in medical research, risk management in business, stock market prediction in finance, data analysis in bioinforma
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Al-Sultany, Ghaidaa Abdalhussein Billal. "Automatic message annotation and semantic interface for context aware mobile computing." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/6564.

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In this thesis, the concept of mobile messaging awareness has been investigated by designing and implementing a framework which is able to annotate the short text messages with context ontology for semantic reasoning inference and classification purposes. The annotated metadata of text message keywords are identified and annotated with concepts, entities and knowledge that drawn from ontology without the need of learning process and the proposed framework supports semantic reasoning based messages awareness for categorization purposes. The first stage of the research is developing the framewor
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Ching-Lin, Lin, and 林敬霖. "Kernel Intuitionistic Fuzzy C-Means Clustering Algorithms with Rough Set for Customer Analysis." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/69926836270687990934.

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碩士<br>龍華科技大學<br>資訊管理系碩士班<br>101<br>Fuzzy C-mean (FCM) algorithms have been widely used in variety of different places. This paper proposes a kernel intuitionistic fuzzy c-means clustering algorithms with rough set (KIFCMRS), and this method is applied to the E-learning data analysis. The rule generation can be divided into two stages for effective rule generation. In the first stage, KIFCM takes advantages of kernel function and intuitionistic fuzzy sets to cluster raw data into similarity groups. In the second stage, the rough set theory is employed to generate rules with different groups. Fi
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Peng, Jen-pin, and 彭仁賓. "The Study of Optimizing Multi-response Problems with Intuitionistic Fuzzy Set." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/79486084912217317899.

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博士<br>國立中央大學<br>機械工程學系<br>103<br>The Taguchi method provides an effective framework for improving quality in industry. However, it determines the optimal setting of process parameters according to only single response. For the sake of optimizing multi-response problems, multiple criteria decision making (MCDM) methods have been extensively utilized in recent years. In considering an engineer's opinion in optimizing a multi-response problem, it must be paid to vagueness and hesitancy in revealing his or her perceptions of a fuzzy concept such as 'importance' or 'excellence'. Recently, the notio
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KUMARI, RANJEETA, and SHIVAM SHARMA. "HIERARCHICAL CLUSTERING OF PICTURE FUZZY RELATION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20420.

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The paper aims to find the (𝛼̃,𝛽̃𝛾̃) - cuts of picture fuzzy relation and apply it to find its hierarchical clustering. we studied the previous results of Intuitionistic fuzzy relation and its (𝛼̃,𝛽̃,𝛾̃) - cuts. We propose a technique of finding 𝛼̃ - cuts of picture fuzzy sets using results of intuitionistic fuzzy set. Membership of PFS mainly deals with positive, negative and neutral membership while IFS only deals with positive and negative membership. In this paper, we attempt to study picture fuzzy set more deeply and providing the more results in picture fuzzy relation that co
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Chang, Jin-He, and 張晉赫. "Fuzzy Rough Set Theory and its Application for Database Marketing." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/55345139412312189102.

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碩士<br>南華大學<br>資訊管理學研究所<br>92<br>In the drastic competition environment, it is an important task that the enterprise must give different marketing strategies based on the different targeting customers to raise its competitive advantages. In the present market, the marketing strategy of the sale by merchandise combination is in widespread use by the enterprise. The enterprise can gain a lot of profit from recommending the relevant merchandise combinations to the targeting customer to promote the consumption desire of the customers. With the help of the knowledge discovered from the databases, th
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Chia-Yi, Chien, and 簡嘉毅. "An Improved Cross-Entropy Approach for Pattern Recognition Based on Intuitionistic Fuzzy Set." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/98868305678012283391.

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碩士<br>國防大學管理學院<br>運籌管理學系<br>97<br>The thesis addresses the issue of information-theoretic discrimination measures for intuitionistic fuzzy sets (IFSs). Although many measures of distance, similarity, dissimilarity, and correlation between IFSs have been proposed, there is no reference regarding information-driven measures used for comparison between sets. In this work, we introduce the concepts of discrimination information and cross-entropy in the intuitionistic fuzzy sets and improve non-probabilistic entropy proposed by Vlachos & Sergiadis (2007) for IFSs. Based on this entropy measure, we
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Kabir, Sohag, T. K. Goek, M. Kumar, M. Yazdi, and F. Hossain. "A method for temporal fault tree analysis using intuitionistic fuzzy set and expert elicitation." 2019. http://hdl.handle.net/10454/17992.

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Yes<br>Temporal fault trees (TFTs), an extension of classical Boolean fault trees, can model time-dependent failure behaviour of dynamic systems. The methodologies used for quantitative analysis of TFTs include algebraic solutions, Petri nets (PN), and Bayesian networks (BN). In these approaches, precise failure data of components are usually used to calculate the probability of the top event of a TFT. However, it can be problematic to obtain these precise data due to the imprecise and incomplete information about the components of a system. In this paper, we propose a framework that combines
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Wu, Pei-Shan, and 吳佩珊. "Apply Adaptive Fuzzy Rough Set and Genetic Algorithm into the Supplier Selection." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/19138124596314569189.

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碩士<br>國立彰化師範大學<br>資訊管理學系所<br>98<br>Effective Supply Chain Management (SCM) can respond with the customers’ instant demand, lower the production cost, and enhance the competitive strength of the enterprise. Thus, how to evaluate and select the appropriate suppliers into the supply chain plays a critical role that determines the success or not of SCM. A plenty of research about the supplier selection had been conducted; however, most of them put attention on the supplier selection issues on the scope of whole supply chain system. Few researches have focused on how an individual company selects t
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Books on the topic "Rough Intuitionistic Fuzzy Set"

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Som, Tanmoy, Oscar Castillo, Anoop Kumar Tiwari, and Shivam Shreevastava, eds. Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8566-9.

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F, Peters James, ed. Transactions on rough sets. Springer, 2004.

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Jensen, Richard. Computational intelligence and feature selection: Rough and fuzzy approaches. Wiley, 2008.

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International Conference on Rough Sets, Fuzzy Sets and Soft Computing (2009 Dept. of Mathematics, Tripura University). Proceedings, International Conference on Rough Sets, Fuzzy Sets, and Soft Computing, November 5-7, 2009. Serials Publications, 2011.

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Vluymans, Sarah. Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04663-7.

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Roman, Słowiński, ed. Intelligent decision support: Handbook of applications and advances of the rough sets theory. Kluwer Academic Publishers, 1992.

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Cao, Bing-Yuan. Optimal Models and Methods with Fuzzy Quantities. Springer-Verlag Berlin Heidelberg, 2010.

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Tiwari, Anoop Kumar, Shivam Shreevastava, Oscar Castillo, and Tanmoy Som. Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling: Theory and Applications. Springer, 2023.

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Chaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley & Sons, Incorporated, John, 2019.

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Chaira, Tamalika. Fuzzy Set and Its Extension: The Intuitionistic Fuzzy Set. Wiley, 2019.

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Book chapters on the topic "Rough Intuitionistic Fuzzy Set"

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Mukherjee, Anjan. "Soft Rough Intuitionistic Fuzzy Sets." In Generalized Rough Sets. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2458-7_3.

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Bandyopadhyay, Sibasis, Zbigniew Suraj, and Piotr Grochowalski. "Modified Generalized Weighted Fuzzy Petri Net in Intuitionistic Fuzzy Environment." In Rough Sets. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47160-0_31.

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Wu, Le-tao, and Xue-hai Yuan. "Intuitionistic Fuzzy Rough Set Based on the Cut Sets of Intuitionistic Fuzzy Set." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66514-6_4.

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Zhang, Yanqin, and Xibei Yang. "An Intuitionistic Fuzzy Dominance–Based Rough Set." In Bio-Inspired Computing and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24553-4_88.

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Wu, Wei-Zhi, Shen-Ming Gu, Tong-Jun Li, and You-Hong Xu. "Intuitionistic Fuzzy Rough Approximation Operators Determined by Intuitionistic Fuzzy Triangular Norms." In Rough Sets and Knowledge Technology. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11740-9_60.

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Yang, Xiaoping, and Anhui Tan. "Three-Way Decisions Based on Intuitionistic Fuzzy Sets." In Rough Sets. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60840-2_21.

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Mukherjee, Anjan. "IF Parameterised Intuitionistic Fuzzy Soft Set Theories on Decisions-Making." In Generalized Rough Sets. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2458-7_10.

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Liu, Guilong. "Closures of Intuitionistic Fuzzy Relations." In Rough Sets and Knowledge Technology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02962-2_35.

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Mukherjee, Anjan. "Interval-Valued Intuitionistic Fuzzy Soft Rough Sets." In Generalized Rough Sets. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2458-7_4.

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Mukherjee, Anjan. "Interval-Valued Intuitionistic Fuzzy Soft Topological Spaces." In Generalized Rough Sets. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2458-7_5.

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Conference papers on the topic "Rough Intuitionistic Fuzzy Set"

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Singh, Shivani. "Enhanced Missing Data Imputation Using Intuitionistic Fuzzy Rough-Nearest Neighbor Approach." In 16th International Conference on Fuzzy Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013015600003837.

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Xu, Yon-Hong, and Wei-Zhi Wu. "On intuitionistic fuzzy rough set algebras." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580541.

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Lu, Yanli, Yingjie Lei, and Yang Lei. "Intuitionistic fuzzy rough set based on intuitionistic similarity relation." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597422.

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Kumar, Deepak, and S. B. Singh. "Evaluating fuzzy reliability using rough intuitionistic fuzzy set." In 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their Impact on Humanity (CIPECH). IEEE, 2014. http://dx.doi.org/10.1109/cipech.2014.7019037.

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Dai, Tran Thanh, Nguyen Long Giang, Hoang Thi Minh Chau, and Tran Thi Ngan. "APPROACH FOR ATTRIBUTE SUBSET SELECTION BASED INTUITIONISTIC FUZZY-ROUGH SET." In HỘI NGHỊ KHOA HỌC CÔNG NGHỆ QUỐC GIA LẦN THỨ XIII NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN. Publishing House for Science and Technology, 2020. http://dx.doi.org/10.15625/vap.2020.00208.

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Kala, R., and P. Deepa. "Intuitionistic Fuzzy C-Means Clustering Using Rough set for MRI Segmentation." In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT). IEEE, 2018. http://dx.doi.org/10.1109/icctct.2018.8550853.

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Qing Guo, Shan-Lin Yang, and Lei Wu. "Multi-granulation rough set model in intuitionistic fuzzy-valued information system." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890469.

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Li, Zhiming, and Yongzhong Tang. "The matrix representation of fuzzy rough set model in intuitionistic fuzzy ordered information system." In 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018. http://dx.doi.org/10.1109/ccdc.2018.8407487.

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Yang, Hua, Weixia Li, Chengyi Zhang, and Juan Li. "A New Definition of Intuitionistic Fuzzy Rough Set and Its Similarity Measure." In 2012 International Conference on Computer Science and Service System (CSSS). IEEE, 2012. http://dx.doi.org/10.1109/csss.2012.461.

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Liu, Xinrui, Xinying Zhao, Peng Jin, and Tianqi Lu. "Optimization Strategy for New Energy Consumption Based on Intuitionistic Fuzzy Rough Set Theory." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189631.

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