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Journal articles on the topic 'Weighted Rough Set'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Das, Pratulananda, Sanjoy Ghosal, Avishek Ghosh, and Sumit Som. "Characterization of rough weighted statistical limit set." Mathematica Slovaca 68, no. 4 (2018): 881–96. http://dx.doi.org/10.1515/ms-2017-0152.

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Abstract Our focus is to generalize the definition of the weighted statistical convergence in a wider range of the weighted sequence {tn}n∈ℕ. We extend the concept of weighted statistical convergence and rough statistical convergence to renovate a new concept namely, rough weighted statistical convergence. On a continuation we also define rough weighted statistical limit set. In the year (2008) Aytar established the following results: The diameter of rough statistical limit set of a real sequence is ≤ 2r (where r is the degree of roughness) and in general it has no smaller bound. If the rough

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2

Ghosal, Sanjoy, та Avishek Ghosh. "Rough weighted 𝓘-limit points and weighted 𝓘-cluster points in θ-metric space". Mathematica Slovaca 70, № 3 (2020): 667–80. http://dx.doi.org/10.1515/ms-2017-0380.

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AbstractIn 2018, Das et al. [Characterization of rough weighted statistical statistical limit set, Math. Slovaca 68(4) (2018), 881–896] (or, Ghosal et al. [Effects on rough 𝓘-lacunary statistical convergence to induce the weighted sequence, Filomat 32(10) (2018), 3557–3568]) established the result: The diameter of rough weighted statistical limit set (or, rough weighted 𝓘-lacunary limit set) of a sequence x = {xn}n∈ℕ is $\begin{array}{} \frac{2r}{{\liminf\limits_{n\in A}} t_n} \end{array}$ if the weighted sequence {tn}n∈ℕ is statistically bounded (or, self weighted 𝓘-lacunary statistically bou

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Ghosal, Sanjoy, and Avishek Ghosh. "When deviation happens between rough statistical convergence and rough weighted statistical convergence." Mathematica Slovaca 69, no. 4 (2019): 871–90. http://dx.doi.org/10.1515/ms-2017-0275.

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Abstract In this paper we introduce rough weighted statistical limit set and weighted statistical cluster points set which are natural generalizations of rough statistical limit set and statistical cluster points set of double sequences respectively. Some new examples are constructed to ensure the deviation of basic results. Both the sets don’t follow the usual extension properties which will be discussed here.

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Azim, Ahmad Bin, Ahmad ALoqaily, Asad Ali, Sumbal Ali, Nabil Mlaiki, and Fawad Hussain. "q-Spherical fuzzy rough sets and their usage in multi-attribute decision-making problems." AIMS Mathematics 8, no. 4 (2023): 8210–48. http://dx.doi.org/10.3934/math.2023415.

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<abstract> <p>This article's purpose is to investigate and generalize the concepts of rough set, in addition to the q-spherical fuzzy set, and to introduce a novel concept that is called q-spherical fuzzy rough set (q-SFRS). This novel approach avoids the complications of more recent ideas like the intuitionistic fuzzy rough set, Pythagorean fuzzy rough set, and q-rung orthopair fuzzy rough set. Since mathematical operations known as "aggregation operators" are used to bring together sets of data. Popular aggregation operations include the arithmetic mean and the weighted mean. The

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Sangeetha, Tamilarasu, and Amalanathan Geetha Mary. "Rough set-based entropy measure with weighted density outlier detection method." Open Computer Science 12, no. 1 (2022): 123–33. http://dx.doi.org/10.1515/comp-2020-0228.

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Abstract The rough set theory is a powerful numerical model used to handle the impreciseness and ambiguity of data. Many existing multigranulation rough set models were derived from the multigranulation decision-theoretic rough set framework. The multigranulation rough set theory is very desirable in many practical applications such as high-dimensional knowledge discovery, distributional information systems, and multisource data processing. So far research works were carried out only for multigranulation rough sets in extraction, selection of features, reduction of data, decision rules, and pa

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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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7

Ghosal, Sanjoy, and Sumit Som. "Different behaviors of rough weighted statistical limit set under unbounded moduli." Filomat 32, no. 7 (2018): 2583–600. http://dx.doi.org/10.2298/fil1807583g.

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In this paper we introduce f-rough weighted statistical limit set and f-weighted statistical cluster points set which are natural generalizations of rough statistical limit set and f-statistical cluster points set of sequence respectively. Some new examples are constructed to ensure the deviation of basic results. So both the sets don?t follow the nature of usual extension properties which will be discussed here.

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Ghosal, Sanjoy, Sourav Mandal, and Mandobi Banerjee. "Approximity of asymmetric metric spaces." Mathematica Slovaca 72, no. 5 (2022): 1227–44. http://dx.doi.org/10.1515/ms-2022-0084.

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Abstract In this present work, we perceive the ideas of rough weighted statistical limit set as well as rough weighted statistical cluster points set and originate these conceptions into asymmetric metric spaces. On this context we frame out several results which substantially intensify these perceptions. While explicating such notions in terms of their asymmetric concepts, this generalization despite unfollows some previous results rather generates new characteristics. Also, we will adorn a sufficient condition using rough weighted statistical convergence which converts asymmetric metric spac

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9

Ghosal, Sanjoy, and Mandobi Banerjee. "Effects on rough I-lacunary statistical convergence to induce the weighted sequence." Filomat 32, no. 10 (2018): 3557–68. http://dx.doi.org/10.2298/fil1810557g.

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Two classes of sets are introduced: rough weighted I-lacunary statistical limit set and weighted I-lacunary statistical cluster points set which are natural generalizations of rough I-limit set and I-cluster points set respectively. To highlight the variation from basic results we place into some new examples. So our aim is to analyze the different behaviors of the new convergences and characterize both the sets with topological approach like closedness, boundedness, compactness etc.

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Tang, Lina, Jian Tang, and Peng Ding. "Establishment of Architectural Heritage Evaluation Indicator System Based on Cluster Analysis in the Era of Big Data." Wireless Communications and Mobile Computing 2022 (April 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/9211435.

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In the era of big data, the data is collected and applied in every aspect of life. Establishing a reasonable architectural heritage evaluation indicators system is the key of architectural heritage evaluation. According to the connotation of architectural heritage and the standard of eliminating duplicate information and the standard of the maximum weighted R cluster grade, this paper constructs an architectural heritage evaluation indicators system through quantitative approaches of R cluster and rough set analysis. The contribution lies in the following: Firstly, it uses the method of square

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Kumar, S. Udhaya, Ahmad Taher Azar, H. Hannah Inbarani, O. Joseph Liyaskar, and Khaled Mohamad Almustafa. "Weighted Rough Set Theory for Fetal Heart Rate Classification." International Journal of Sociotechnology and Knowledge Development 11, no. 4 (2019): 1–19. http://dx.doi.org/10.4018/ijskd.2019100101.

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A novel weighted rough set-based classification approach is introduced for the evaluation of fetal nature acquired from a CardioTocoGram (CTG) signal. The classification is essential to anticipate newborn's well-being, particularly for the life-threatening cases. CTG monitoring comprises of electronic fetal heart rate (FHR), fetal activities and the uterine contraction (UC) signals. These signals are extensively used as a part of the pregnancy and give extremely significant data on fetal health. The obtained data from these recordings can be utilized to anticipate the condition of the newborn

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Ghosal, Sanjoy, M. C. Listán-García, Manasi Mandal та Mandobi Banerjee. "Influence of θ-metric spaces on the diameter of rough weighted I2-limit set". Filomat 34, № 3 (2020): 737–50. http://dx.doi.org/10.2298/fil2003737g.

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In this paper we continue our investigation of the recent summability notion introduced in [Math. Slovaca 69 (4) (2019) 871-890] (where rough weighted statistical convergence for double sequences is discussed over norm linear spaces) and introduce the notion of rough weighted I2-convergence over ?-metric spaces. Also we exercise the behavior of weighted I2-cluster points set over ?-metric spaces. Based on the new notion we vividly discuss some important results and perceive how the existing results are vacillating.

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Yang, Fan, and Cai Li Zhang. "Research on Rough Set Based Weighted Grey Fault Diagnosis Algorithm and Practical Application." Applied Mechanics and Materials 65 (June 2011): 272–75. http://dx.doi.org/10.4028/www.scientific.net/amm.65.272.

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Considering the poor accuracy of grey relational analysis method in fault diagnosis field, a rough set based weighted grey fault diagnosis algorithm is provided. The algorithm extracts the core characteristic parameters described the fault description and calculate their weight with rough sets and its attribute reduction method, the possible fault pattern of the unknown pattern is judged by the relation degree which calculated quantitatively with each typical fault pattern in history diagnosis record based on weighted grey relational analysis method. Experiment result of recognizing a diesel e

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Moslemnejad, Somaye, and Javad Hamidzadeh. "Weighted support vector machine using fuzzy rough set theory." Soft Computing 25, no. 13 (2021): 8461–81. http://dx.doi.org/10.1007/s00500-021-05773-7.

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15

Jinghong, Fu, Zhang Chunying, Wang Jing, and Tian Fang. "A Weighted Relational Classification Algorithm Based on Rough Set." International Journal of Education and Management Engineering 3, no. 2 (2013): 20–25. http://dx.doi.org/10.5815/ijeme.2013.02.04.

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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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Li, Xiangjun, Fen Rao, Tinghua Wang, and Taorong Qiu. "Rough Set-Based Feature Weighted Kernels for Support Vector Machine." Journal of Computational and Theoretical Nanoscience 9, no. 12 (2012): 2250–54. http://dx.doi.org/10.1166/jctn.2012.2646.

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Rogulj, Katarina, Jelena Kilić Pamuković, and Majda Ivić. "Hybrid MCDM Based on VIKOR and Cross Entropy under Rough Neutrosophic Set Theory." Mathematics 9, no. 12 (2021): 1334. http://dx.doi.org/10.3390/math9121334.

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Problems in real life usually involve uncertain, inconsistent and incomplete information. An example of such problems is strategic decision making with respect to remediation planning of historic pedestrian bridges. The multiple decision makers and experts, as well as the various mutually conflicting criteria, unknown criteria weights, and vagueness and duality in final decisions, provide motivation to develop a methodology that is able to resist the challenges implicit in this problem. Therefore, the aim of this research was to propose an algorithm based on the theory of rough neutrosophic se

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Liu, Jia-Bao, Rashad Ismail, Muhammad Kamran, Esmail Hassan Abdullatif Al-Sabri, Shahzaib Ashraf, and Ismail Naci Cangul. "An optimization strategy with SV-neutrosophic quaternion information and probabilistic hesitant fuzzy rough Einstein aggregation operator." AIMS Mathematics 8, no. 9 (2023): 20612–53. http://dx.doi.org/10.3934/math.20231051.

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<abstract><p>The single valued neutrosophic probabilistic hesitant fuzzy rough Einstein aggregation operator (SV-NPHFRE-AO) is an extension of the neutrosophic probabilistic hesitant fuzzy rough set theory. It is a powerful decision-making tool that combines the concepts of neutrosophic logic, probability theory, hesitant fuzzy sets, rough sets, and Einstein aggregation operators. SV-NPHFRE-AO can be applied in many fields, including livestock decision making. Making judgments about a wide range of issues, including feed formulation, breeding program design, disease diagnostics, an

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Sagar, Shrikant Brajesh, and Akhilesh Tiwari. "Rough Set and Genetic based Model for Extracting Weighted Association Rules." International Journal of Hybrid Information Technology 8, no. 11 (2015): 121–38. http://dx.doi.org/10.14257/ijhit.2015.8.11.10.

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LIU, J., Q. HU, and D. YU. "A weighted rough set based method developed for class imbalance learning." Information Sciences 178, no. 4 (2008): 1235–56. http://dx.doi.org/10.1016/j.ins.2007.10.002.

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Zhang, Fu, Weimin Ma, and Hongwei Ma. "Dynamic Chaotic Multi-Attribute Group Decision Making under Weighted T-Spherical Fuzzy Soft Rough Sets." Symmetry 15, no. 2 (2023): 307. http://dx.doi.org/10.3390/sym15020307.

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In this article, the parameter of the decision maker's familiarity with the attributes of the alternatives is introduced for the first time in dynamic multi-attribute group decision making to avoid the disadvantages arising from the inappropriate grouping of decision makers. We combine it with fuzzy soft rough set theory and dynamic multi-attribute-grouping decision making to obtain a new decision model, i.e., dynamic chaotic multiple-attribute group decision making. Second, we provide an algorithm for solving this model under a weighted T-spherical fuzzy soft rough set, which can not only ach

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Zhang, Fu, and Weimin Ma. "Study on Chaotic Multi-Attribute Group Decision Making Based on Weighted Neutrosophic Fuzzy Soft Rough Sets." Mathematics 11, no. 4 (2023): 1034. http://dx.doi.org/10.3390/math11041034.

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In this article, we have proposed a multi-attribute group decision making (MAGDM) with a new scenario or new condition named Chaotic MAGDM, in which not only the weights of the decision makers (DMs) and the weights of the decision attributes are considered, but also the familiarity of the DMs with the attributes are considered. Then we applied the weighted neutrosophic fuzzy soft rough set theory to Chaotic MAGDM and proposed a new algorithm for MAGDM. Moreover, we provide a case study to demonstrate the application of the algorithm. Our contributions to the literature are as follows: (1) fami

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Alias, Suriana, Daud Mohamad, Adibah Shuib, et al. "Rough Neutrosophic Multisets Geometric Aggregation Operator with Entropy Weight Combined Roughness Dice Similarity Measure and Its Application." ITM Web of Conferences 67 (2024): 01026. http://dx.doi.org/10.1051/itmconf/20246701026.

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Rough neutrosophic multisets (RNM) is an uncertainty set theory generalized from the rough neutrosophic set. In the same equivalence relation, the universal set is a neutrosophic multisets with boundary regions involving lower and upper approximation. To date, to handle the multiplicity of information collected, the rough neutrosophic multisets geometric aggregation operator (RNMGAO) is introduced. The algebraic operations of RNM used in the derivation of RNMGAO are defined. The entropy measure of RNM is also discussed as a weighted assign for each criterion simultaneously with the geometric a

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Mahmood, Tahir, Ahmad Idrees, Khizar Hayat, Muhammad Ashiq, and Ubaid ur Rehman. "Selection of AI Architecture for Autonomous Vehicles Using Complex Intuitionistic Fuzzy Rough Decision Making." World Electric Vehicle Journal 15, no. 9 (2024): 402. http://dx.doi.org/10.3390/wevj15090402.

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The advancement of artificial intelligence (AI) has become a crucial element in autonomous cars. A well-designed AI architecture will be necessary to attain the full potential of autonomous vehicles and will significantly accelerate the development and deployment of autonomous cars in the transportation sector. Promising autonomous cars for innovating modern transportation systems are anticipated to address many long-standing transporting challenges related to congestion, safety, parking, and energy conservation. Choosing the optimal AI architecture for autonomous vehicles is a multi-attribute

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Wang, Yinyu, Azmat Hussain, Tahir Mahmood, Muhammad Irfan Ali, Hecheng Wu, and Yun Jin. "Decision-Making Based on q-Rung Orthopair Fuzzy Soft Rough Sets." Mathematical Problems in Engineering 2020 (December 15, 2020): 1–21. http://dx.doi.org/10.1155/2020/6671001.

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Recently, Yager presented the new concept of q-rung orthopair fuzzy (q-ROF) set (q-ROFS) which emerged as the most significant generalization of Pythagorean fuzzy set (PFS). From the analysis of q-ROFS, it is clear that the rung q is the most significant feature of this notion. When the rung q increases, the orthopair adjusts in the boundary range which is needed. Thus, the input range of q-ROFS is more flexible, resilient, and suitable than the intuitionistic fuzzy set (IFS) and PFS. The aim of this manuscript is to investigate the hybrid concept of soft set ( S t S) and rough set with the no

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Own, Hala S., and Ajith Abraham. "A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice." Applied Soft Computing 12, no. 3 (2012): 999–1005. http://dx.doi.org/10.1016/j.asoc.2011.11.025.

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Liang, Sun, and Han Chongzhao. "Dynamic weighted voting for multiple classifier fusion: A generalized rough set method." Journal of Systems Engineering and Electronics 17, no. 3 (2006): 487–94. http://dx.doi.org/10.1016/s1004-4132(06)60084-4.

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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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Joshi, Vishwas Deep, Saurabh Singh Naharwar, Jagdev Singh, and Kottakkaran Sooppy Nisar. "A new solution procedure for multi-objective linear fractional transportation problem with rough parameters." Journal of Interdisciplinary Mathematics 26, no. 1 (2023): 121–32. http://dx.doi.org/10.47974/jim-1651.

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In this article, we are discussing a rough multi-objective linear fractional transportation problem (RMOLFTP). In Day-to-day life, we frequently encounter many situations where the transportation problems are represented by a rough set. The main aim of our research article is to solve the RMOLFTP where the transportation cost and profit, of the product as rough interval coefficients. Here a new logic is introduced by us to solve RMOLFTP that diversifies the form of Nomani et. al., (2016). To demonstrate the recommended algorithm, a rough numeric problem with two objective functions is offered.

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Hatem Jabbar, Hiyam, Raed Majeed Muttasher, and Ali Fattah Dakhil. "Segmentation of brain tissue using improved kernelized rough-fuzzy c-means technique." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (2023): 216. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp216-226.

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<p><span>Brain magnetic resonance imaging (MRI) data is a hot topic in the domains of biomedical engineering and machine learning. Without locating anomalies, such as tumors and edema, radiologists and other medical experts cannot effectively recommend or administer therapy for patients. Having three different magnetic resonance techniques (T1 weighted, T2 weighted, and T3 weighted), MRI can produce detailed multimodal scans of different human brain tissues with varying contrast, which can help pinpoint the source of any abnormalities. The cerebrospinal fluid (CSF), white matter (W

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Garg, Harish, Sultan S. Alodhaibi, and Hamiden Abd El-Wahed Khalifa. "Study on multi-objective nonlinear programming problem with rough parameters." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 3591–604. http://dx.doi.org/10.3233/jifs-211747.

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Rough set theory, introduced by Pawlak in 1981, is one of the important theories to express the vagueness not by means of membership but employing a boundary region of a set, i.e., an object is approximately determined based on some knowledge. In our real-life, there exists several parameters which impact simultaneously on each other and hence dealing with such different parameters and their conflictness create a multi-objective nonlinear programming problem (MONLPP). The objective of the paper is to deal with a MONLPP with rough parameters in the constraint set. The considered MONLPP with rou

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WANG, SHU-LIN, XUELING LI, JUN-FENG XIA, and Xiao-Ping Zhang. "WEIGHTED NEIGHBORHOOD CLASSIFIER FOR THE CLASSIFICATION OF IMBALANCED TUMOR DATASET." Journal of Circuits, Systems and Computers 19, no. 01 (2010): 259–73. http://dx.doi.org/10.1142/s0218126610006232.

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Machine learning is widely applied to gene expression profiles based molecular tumor classification, but sample imbalance problem is often overlooked. This paper proposed a subclass-weighted neighborhood classifier to address the imbalanced sample set problem and a novel neighborhood rough set model to select informative genes for classification performance improvement. Experiments on three publicly available tumor datasets demonstrated that the proposed method is obviously effective on imbalanced dataset with obscure boundary between two subtypes and informative gene selection and it can achi

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Li, Juanli, and Zhaojian Yang. "An Improved Algorithm of Extracting Fault Diagnosis Rules Based on Rough Sets." Open Mechanical Engineering Journal 8, no. 1 (2014): 285–91. http://dx.doi.org/10.2174/1874155x01408010285.

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Rough set is a new data analysis theory. It is often used to deal with fuzzy and uncertain problems. Attribution reduction is the key step in obtaining the knowledge by utilizing rough set. An improved heuristic reduction algorithm of attribute significance is proposed in the study based on analyzing the classic knowledge acquisition method of rough set theory. The algorithm corrects the discernibility matrix and redefines the calculation method of attribute importance. Then it fuses the both, gets the core by using the revised method of discernibility matrix and calculates the attribute impor

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Wang, Tongtong, and Jianing Cui. "Modelling Enterprise’s Coordinated Development Strategy with a Soft Fuzzy Rough Set." Discrete Dynamics in Nature and Society 2021 (June 23, 2021): 1–7. http://dx.doi.org/10.1155/2021/3250306.

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With the concept of sustainable development, enterprises are facing severe challenges in ecological protection and economic development. Approaches to improving effectiveness of the coordinated development strategy must continue to evolve to address uncertainty and hazards that may be encountered in the future. We propose a coordinated development strategy model based on the combination of soft fuzzy and rough set theory and construct its prediction model. For the multistrategy dataset in the paper, parameter for each kind shall be selected through converting the multistrategy data into two pr

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Rekha, S. Nithya, and C. Chandrasekar. "Energy efficient routing to reduce flooding in weighted rough set model using MANET." International Journal of Communication Networks and Distributed Systems 13, no. 1 (2014): 83. http://dx.doi.org/10.1504/ijcnds.2014.063987.

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Attaullah, Shahzaib Ashraf, Noor Rehman, Hussain AlSalman, and Abdu H. Gumaei. "A Decision-Making Framework Using q-Rung Orthopair Probabilistic Hesitant Fuzzy Rough Aggregation Information for the Drug Selection to Treat COVID-19." Complexity 2022 (February 4, 2022): 1–37. http://dx.doi.org/10.1155/2022/5556309.

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In our current era, a new rapidly spreading pandemic disease called coronavirus disease (COVID-19), caused by a virus identified as a novel coronavirus (SARS-CoV-2), is becoming a crucial threat for the whole world. Currently, the number of patients infected by the virus is expanding exponentially, but there is no commercially available COVID-19 medication for this pandemic. However, numerous antiviral drugs are utilized for the treatment of the COVID-19 disease. Identification of the appropriate antivirus medicine to treat the infection of COVID-19 is still a complicated and uncertain decisio

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Li, Ling Ling, Ming Hui Wang, Fei Zheng, and Yong Guang Duanmu. "Methods of Assigning Attribute Weights Based on Rough Set and D-S Evidential Theory." Key Engineering Materials 419-420 (October 2009): 249–52. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.249.

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Using the concept of support degree in rough set theory for reference, two new concepts, namely dominant support degree and recessive support degree were proposed in this paper. By utilizing these new concepts, two simple weight assignment methods by which dominant weight and recessive weight of attributes could be obtained. Nonetheless both the methods had drawbacks. Hence a further method was developed below. The first step was to construct two items of evidence by deploying dominant weights vector and recessive weights vector of the attribute set and then assigned weights for the evidence t

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Wang, Ning, and Enhui Zhao. "A new method for feature selection based on weighted k-nearest neighborhood rough set." Expert Systems with Applications 238 (March 2024): 122324. http://dx.doi.org/10.1016/j.eswa.2023.122324.

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Nur Qafareeny Abdul Halim, Noor Azzah Awang, Siti Nurhidayah Yaacob, Hazwani Hashim, and Lazim Abdullah. "Rough Neutrosophic Shapley Weighted Einstein Averaging Aggregation Operator and its Application in Multi-Criteria Decision-Making Problem." Journal of Advanced Research in Applied Sciences and Engineering Technology 43, no. 2 (2024): 52–64. http://dx.doi.org/10.37934/araset.43.2.5264.

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The study of aggregation operators has played a crucial role in various decision-making methods. The primary function of the aggregation operator is to combine multiple numbers into a single value. While Einstein operators offer a compact notation and can handle complex and large datasets, they do not consider the interaction involved in determining the criteria weights or account for imprecise and indeterminate data. To overcome this limitation, this paper introduces an improved aggregation operator, the rough neutrosophic Shapley weighted Einstein averaging aggregation operator. The rough ne

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Ren, Min, Zhihao Wang, and Guangfen Yang. "A Self-Adaptive Weighted Fuzzy c-Means for Mixed-Type Data." International Journal of Computational Intelligence and Applications 19, no. 04 (2020): 2050030. http://dx.doi.org/10.1142/s1469026820500303.

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The influence of features on each cluster is not the same in a mixed-type dataset. Based on the rough set and shadow set theories, the fuzzy distribution centroid was defined to represent the clustering center of the discrete feature so that the fuzzy c-means algorithm (FCM) could be extended to cluster the data with both continuous and discrete features. Then, considering the different contributions of the features to each cluster, a new weighted objective function was constructed in accordance with the principles of fuzzy compactness and separation. Because the learning feature weight is the

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Zhou, Xi, and Ke Luo. "A Weighted Naive Bayes Algorithm Based on the Attribute Order Reduction." Advanced Materials Research 718-720 (July 2013): 2108–12. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2108.

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Naïve Bayes classifier was generally considered as a simple and efficient classification method. However, its classification performance was affected to some extent because of the assuming that the conditions properties were independent of each other. By analyzing the classification principle and improvement of Bayesian and the Attribute Reduction of Rough Set, this paper proposed a Naïve Bayes algorithm that the attribute order reduction and weighting were improved simultaneously. Experiment results demonstrated that the proposed method performed well in classification accuracy.

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Wang, Tianxiong, and Meiyu Zhou. "Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 331–53. http://dx.doi.org/10.3233/jifs-201829.

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When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory

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Jun, Wang, Tang Lingyu, Zhang Xianyong, and Luo Yuyan. "Three-way weighted combination-entropies based on three-layer granular structures." Applied Mathematics and Nonlinear Sciences 2, no. 2 (2017): 329–40. http://dx.doi.org/10.21042/amns.2017.2.00027.

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AbstractRough set theory is an important theory for the uncertain information processing. The information theoretic measures have been introduced into rough set theory and provided a new effective method in uncertainty measurement and attribute reduction. However, most of them did not consider the hierarchical structure of a decision table (D-Table). Thus, this paper concretely constructs three-way weighted combination-entropies based on the D-Table’s three-layer granular structures and Bayes’ theorem from a new perspective, and reveals the granulation monotonicity and systematic relationships

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Khan, Muhammad Kamraz, Muhammad Sajjad Ali Khan, Kamran, and Ioan-Lucian Popa. "Intuitionistic Hesitant Fuzzy Rough Aggregation Operator-Based EDAS Method and Its Application to Multi-Criteria Decision-Making Problems." Axioms 14, no. 1 (2024): 21. https://doi.org/10.3390/axioms14010021.

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The fundamental notions of the intuitionistic hesitant fuzzy set (IHFS) and rough set (RS) are general mathematical tools that may easily manage imprecise and uncertain information. The EDAS (Evaluation based on Distance from Average Solution) approach has an important role in decision-making (DM) problems, particularly in multi-attribute group decision-making (MAGDM) scenarios, where there are many conflicting criteria. This paper aims to introduce the IHFR-EDAS approach, which utilizes the IHF rough averaging aggregation operator. The aggregation operator is crucial for aggregating intuition

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Liang, Zheng Li, Xiao Wan Yang, and Ke Yao Yu. "Research on Subjective and Objective Comprehensive Evaluation Method Based on AHP and Rough Set." Applied Mechanics and Materials 543-547 (March 2014): 1817–21. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1817.

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In the process of comprehensively evaluating multi-object and making decision, the evaluation method is an important factor that leads the evaluation results. In this paper, we discuss a subjective and objective comprehensive evaluation method based on AHP and rough set, by using which, the weight is determined and the comprehensive weight of the attribute is calculated. Then the data is analyzed by weighted method to obtain the overall ordering results. Finally, we analyze practical examples by the implemented program and verify the effectiveness of the proposed approach.

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Bae, Ihn-Han, Hwa-Ju Lee, and Kyung-Sook Lee. "Design and evaluation of a rough set-based anomaly detection scheme considering weighted feature values." International Journal of Knowledge-based and Intelligent Engineering Systems 11, no. 4 (2007): 201–6. http://dx.doi.org/10.3233/kes-2007-11402.

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Asadi, Yasaman, Najmeh Neysani Samany, Majid Kiavarz Moqadam, Ata Abdollahi Kakroodi, and Meysam Argany. "Seismic vulnerability assessment of urban buildings using the rough set theory and weighted linear combination." Journal of Mountain Science 19, no. 3 (2022): 849–61. http://dx.doi.org/10.1007/s11629-021-6724-4.

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Zheng, Pai, Yuanbin Wang, Xun Xu, and Sheng Quan Xie. "A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes." International Journal of Advanced Manufacturing Technology 91, no. 5-8 (2016): 1977–90. http://dx.doi.org/10.1007/s00170-016-9890-8.

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Ho, Wen-Rong Jerry. "Applied Rough Set Logics for Multi-Criteria Decision Analysis in Stock Index Volatility Projection." Review of Pacific Basin Financial Markets and Policies 14, no. 04 (2011): 715–35. http://dx.doi.org/10.1142/s0219091511002378.

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The main purpose of this paper is to advocate a rule-based forecasting technique for anticipating stock index volatility. This paper intends to set up a stock index indicators projection prototype by using a multiple criteria decision making model consisting of the cluster analysis (CA) technique and Rough Set Theory (RST) to select the important attributes and forecast TSEC Capitalization Weighted Stock Index. The projection prototype was then released to forecast the stock index in the first half of 2009 with an accuracy of 66.67%. The results point out that the decision rules were authentic

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