Relevant bibliographies by topics / Weighted Rough Set

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

Academic literature on the topic 'Weighted Rough Set'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Weighted Rough Set"

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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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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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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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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Dissertations / Theses on the topic "Weighted Rough Set"

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Ku, I.-Chun, and 菰怡君. "Using Variable Weight Grey and Advantages Rough Set for Evaluation on Motorcycle Industry Sales Rate." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/81893803210053462138.

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碩士<br>國立高雄應用科技大學<br>工業工程與管理系碩士班<br>101<br>With Taiwan's economic and social development, motorcycles which are narrowly and densely populated in Taiwan became the simple and convenient mean of transport. Because the motorcycle has reached saturation in Taiwan, the market must be gradually extended out. Taiwan, the Kingdom of motorcycle manufacturing, has many motorcycle brands to choose. In motorcycle sales market, people have preference to a particular brand of motorcycle. From the view of sales, it can show that which motorcycle brands people loves. Therefore, the study will through the moto
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Book chapters on the topic "Weighted Rough Set"

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Liu, Jinfu, and Daren Yu. "A Weighted Rough Set Approach for Cost-Sensitive Learning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72530-5_42.

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Verbiest, Nele, Chris Cornelis, and Francisco Herrera. "OWA-FRPS: A Prototype Selection Method Based on Ordered Weighted Average Fuzzy Rough Set Theory." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41218-9_19.

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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." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11892960_59.

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Yu, Jianhang, Peng Deng, Shunbao Zhao, and Suying Pan. "A Novel Weighted k-Nearest Neighborhood Rough Set Approach for Interval-Valued Decision Information System." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92747-8_5.

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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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Chen, Jie, Yang Li, Shu Zhao, Xiangyang Wang, and Yanping Zhang. "Three-Way Decisions Community Detection Model Based on Weighted Graph Representation." In Rough Sets. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52705-1_11.

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Yuan, Ge, Jie Zhou, and Qiongbin Chen. "Rough-Fuzzy Clustering Based on Adaptive Weighted Values and Three-Way Decisions." In Rough Sets. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21244-4_31.

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Suraj, Zbigniew, Aboul Ella Hassanien, and Sibasis Bandyopadhyay. "Weighted Generalized Fuzzy Petri Nets and Rough Sets for Knowledge Representation and Reasoning." In Rough Sets. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52705-1_5.

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Lenz, Oliver Urs, Daniel Peralta, and Chris Cornelis. "A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators." In Rough Sets. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22815-6_16.

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Xu, Yingge, Yan Yang, Hongjun Wang, and Jie Hu. "An Overlapping Clustering Approach with Correlation Weight." In Rough Sets. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60837-2_49.

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Conference papers on the topic "Weighted Rough Set"

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Zhang, Zhonghao, Jingjing Song, Huige Li, and Eric C. C. Tsang. "A Weighted Granular-Ball Rough Set: Model and Attribute Reduction." In 2024 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2024. https://doi.org/10.1109/icmlc63072.2024.10935042.

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Ma, Tinghuai, and Meili Tang. "Weighted Rough Set Model." In 2006 6th International Conference on Intelligent Systems Design and Applications. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.280.

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Guo, Yanting, Eric C. C. Tsang, and Meng Hu. "Local Weighted Generalized Multigranulation Neighborhood Rough Set." In 2021 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2021. http://dx.doi.org/10.1109/icwapr54887.2021.9736185.

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Wang, Zhiqiang, Tingting Zheng, Qing Li, and Xin Sun. "Weighted Multi-granulation Containment Neighborhood Rough Set Model." In 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2022. http://dx.doi.org/10.1109/wi-iat55865.2022.00134.

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Zhang, Qizhong, and Xugang Xi. "Weighted Clustering Approach Based on Rough Set Similarity Model." In 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI). IEEE, 2010. http://dx.doi.org/10.1109/aici.2010.36.

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Own, Hala S., Nehal Abd Al Aal, and Ajith Abraham. "A new weighted rough set framework for imbalance class distribution." In 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2010. http://dx.doi.org/10.1109/socpar.2010.5685849.

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Wu, Yu, Kun She, Williams Zhu, Xiaojun Yue, and Huiqiong Luo. "A Web Text Filter Based on Rough Set Weighted Bayesian." In 2009 International Conference on Dependable, Autonomic and Secure Computing (DASC). IEEE, 2009. http://dx.doi.org/10.1109/dasc.2009.38.

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Wang, Li-xin, Wen-sheng Li, Xi-sheng Jia, and Hai-kuan Wang. "Ammunition Target Matching Based on Rough Set and Weighted TOPSIS." In 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control (IMCCC). IEEE, 2011. http://dx.doi.org/10.1109/imccc.2011.14.

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Li, Jian-guo, and Jing-wei Gao. "Research on Improved Weighted Fuzzy Clustering Algorithm Based on Rough Set." In 2009 International Conference on Computer Engineering and Technology (ICCET). IEEE, 2009. http://dx.doi.org/10.1109/iccet.2009.236.

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Liu, Jin-Fu, and Da-Ren Yu. "A Weighted Rough Set Method to Address the Class Imbalance Problem." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370789.

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