Relevant bibliographies by topics / Rough Set Theory (RST)

Contents

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

Academic literature on the topic 'Rough Set Theory (RST)'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Rough Set Theory (RST)"

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Dwiputranto, Teguh Handjojo, Noor Akhmad Setiawan, and Teguh Bharata Adji. "Rough-Set-Theory-Based Classification with Optimized k-Means Discretization." Technologies 10, no. 2 (2022): 51. http://dx.doi.org/10.3390/technologies10020051.

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The discretization of continuous attributes in a dataset is an essential step before the Rough-Set-Theory (RST)-based classification process is applied. There are many methods for discretization, but not many of them have linked the RST instruments from the beginning of the discretization process. The objective of this research is to propose a method to improve the accuracy and reliability of the RST-based classifier model by involving RST instruments at the beginning of the discretization process. In the proposed method, a k-means-based discretization method optimized with a genetic algorithm
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Chiaselotti, G., T. Gentile, and F. Infusino. "Decision systems in rough set theory: A set operatorial perspective." Journal of Algebra and Its Applications 18, no. 01 (2019): 1950004. http://dx.doi.org/10.1142/s021949881950004x.

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In rough set theory (RST), the notion of decision table plays a fundamental role. In this paper, we develop a purely mathematical investigation of this notion to show that several basic aspects of RST can be of interest also for mathematicians who work with algebraic and discrete methods.In this abstract perspective, we call decision system a sextuple [Formula: see text] [Formula: see text], where [Formula: see text], [Formula: see text], [Formula: see text] are non-empty sets whose elements are called, respectively, objects, condition attributes, values, [Formula: see text] is a (possibly emp
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Rana Aamir Raza. "An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network." Lahore Garrison University Research Journal of Computer Science and Information Technology 5, no. 3 (2021): 92–108. http://dx.doi.org/10.54692/lgurjcsit.2021.0503224.

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In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy
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Quafafou, M. "α-RST: a generalization of rough set theory". Information Sciences 124, № 1-4 (2000): 301–16. http://dx.doi.org/10.1016/s0020-0255(99)00075-4.

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Manikandan, R., Rajesh Kumar Maurya, Tariq Rasheed, et al. "Adaptive cloud orchestration resource selection using rough set theory." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 311–20. http://dx.doi.org/10.47974/jim-1662.

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The recent research is developing in a vast speed to develop the cloud orchestration system. In cloud system the remotely managed servers are storing, finding, removing, replacing and retrieving the various services in an adaptive optimized manner. The lot of services are provided by the vast number of providers in the market with the help of approximation theory by the rough set system (RST). RST finds in helping in getting the efficient cloud resources as a service to the users. The proposed OCRS (Optimized Cost Resource System) approach is being simulated and compared with the existing clou
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Sun, Xinwei, and Kai Zeng. "RST: Rough Set Transformer for Point Cloud Learning." Sensors 23, no. 22 (2023): 9042. http://dx.doi.org/10.3390/s23229042.

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Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global gui
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Setyaningsih, Nevi, Fitriani Fitriani, and Ahmad Faisol. "Sub-exact sequence of rough groups." Al-Jabar : Jurnal Pendidikan Matematika 12, no. 2 (2021): 267–72. http://dx.doi.org/10.24042/ajpm.v12i2.8917.

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Rough Set Theory (RST) is an essential mathematical tool to deal with imprecise, inconsistent, incomplete information and knowledge Rough Some algebra structures, such as groups, rings, and modules, have been presented on rough set theory. The sub-exact sequence is a generalization of the exact sequence. In this paper, we introduce the notion of a sub-exact sequence of groups. Furthermore, we give some properties of the rough group and rough sub-exact sequence of groups.
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S.Surekha, *. "A COMPARATIVE STUDY OF VARIOUS ROUGH SET THEORY ALGORITHMS FOR FEATURE SELECTION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 21–30. https://doi.org/10.5281/zenodo.495147.

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Machine Learning techniques can be used to improve the performance of intelligent software systems. The performance of any Machine Learning algorithm mainly depends on the quality and relevance of the training data. But, in real world the data is noisy, uncertain and often characterized by a number of features. Existence of uncertainties and the presence of irrelevant features in the high dimensional datasets often degrade the performance of the machine learning algorithms in all aspects. In this paper, the concepts of Rough Set Theory(RST) are applied to remove inconsistencies in data and var
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Alawneh, Tahani Nawaf, and Mehmet Ali Tut. "Using Rough Set Theory to Find Minimal Log with Rule Generation." Symmetry 13, no. 10 (2021): 1906. http://dx.doi.org/10.3390/sym13101906.

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Data pre-processing is a major difficulty in the knowledge discovery process, especially feature selection on a large amount of data. In literature, various approaches have been suggested to overcome this difficulty. Unlike most approaches, Rough Set Theory (RST) can discover data de-pendency and reduce the attributes without the need for further information. In RST, the discernibility matrix is the mathematical foundation for computing such reducts. Although it proved its efficiency in feature selection, unfortunately it is computationally expensive on high dimensional data. Algorithm complex
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Subrata Kumar Nayak, Et al. "Detection of HIV by using Rough Set and Homotopy Analysis Method." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1460–70. http://dx.doi.org/10.17762/ijritcc.v11i10.8696.

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The significant objective of this research is to recognize how to calculate the classification process using rough set theory (RST) for the Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV & AIDS) symptoms dataset. RST has a multi-dimensional concept with multiple approaches. In this paper, our main objective is to find the symptoms of (HIV & AIDS) using basic RST and Homotopy Analysis Method (HAM) to validate our claim using statistical techniques. We prefer RST & HAM over other soft computing techniques and Mathematical Modelling as both RST and
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Dissertations / Theses on the topic "Rough Set Theory (RST)"

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Liu, Yongwen. "Cloud services selection based on rough set theory." Thesis, Troyes, 2016. http://www.theses.fr/2016TROY0018/document.

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Avec le développement du cloud computing, de nouveaux services voient le jour et il devient primordial que les utilisateurs aient les outils nécessaires pour choisir parmi ses services. La théorie des ensembles approximatifs représente un bon outil de traitement de données incertaines. Elle peut exploiter les connaissances cachées ou appliquer des règles sur des ensembles de données. Le but principal de cette thèse est d'utiliser la théorie des ensembles approximatifs pour aider les utilisateurs de cloud computing à prendre des décisions. Dans ce travail, nous avons, d'une part, proposé un cad
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Shahabi, Lotfabadi Maryam. "Using rough set theory to improve content based image retrieval system." Thesis, Shahabi Lotfabadi, Maryam (2016) Using rough set theory to improve content based image retrieval system. PhD thesis, Murdoch University, 2016. https://researchrepository.murdoch.edu.au/id/eprint/33675/.

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Each image in a Content Based Image Retrieval (CBIR) system is represented by its features such as colour, texture and shape. These three groups of features are stored in the feature vector. Therefore, each image managed by the CBIR system is associated with one or more feature vectors. As a result, the storage space required for feature vectors is proportional to the amount of images in the database. In addition, when comparing the similarities among images, the CBIR needs to compare these feature vectors. Nonetheless, researchers are still facing problems when working with a huge image datab
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Andersson, Robin. "Implementation av ett kunskapsbas system för rough set theory med kvantitativa mätningar." Thesis, Linköping University, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1756.

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<p>This thesis presents the implementation of a knowledge base system for rough sets [Paw92]within the logic programming framework. The combination of rough set theory with logic programming is a novel approach. The presented implementation serves as a prototype system for the ideas presented in [VDM03a, VDM03b]. The system is available at "http://www.ida.liu.se/rkbs". </p><p>The presented language for describing knowledge in the rough knowledge base caters for implicit definition of rough sets by combining different regions (e.g. upper approximation, lower approximation, boundary) of other de
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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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Zhang, Xueying. "Rough set theory based automatic text categorization and the handling of semantic heterogeneity." Bonn Informationszentrum Sozialwiss, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2704442&prov=M&dokv̲ar=1&doke̲xt=htm.

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Zhou, Xujuan. "Rough set-based reasoning and pattern mining for information filtering." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/29350/1/Xujuan_Zhou_Thesis.pdf.

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An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relativel
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Zhou, Xujuan. "Rough set-based reasoning and pattern mining for information filtering." Queensland University of Technology, 2008. http://eprints.qut.edu.au/29350/.

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An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relativel
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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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Griffiths, Benjamin. "Variable precision rough set theory decision support system : with an application to bank rating prediction." Thesis, Cardiff University, 2008. http://orca.cf.ac.uk/55175/.

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This dissertation considers, the Variable Precision Rough Sets (VPRS) model, and its development within a comprehensive software package (decision support system), incorporating methods of re sampling and classifier aggregation. The concept of /-reduct aggregation is introduced, as a novel approach to classifier aggregation within the VPRS framework. The software is applied to the credit rating prediction problem, in particularly, a full exposition of the prediction and classification of Fitch's Individual Bank Strength Ratings (FIBRs), to a number of banks from around the world is presented.
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Zielosko, Beata. "Construction and optimization of partial decision rules." Doctoral thesis, Katowice : Uniwersytet Śląski, 2008. http://hdl.handle.net/20.500.12128/5094.

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Tematyka pracy związana jest z badaniem algorytmów zachłannych dla konstruowania i optymalizacji częściowych (przybliżonych) reguł decyzyjnych. Przedstawione w pracy badania dotyczące częściowych reguł decyzyjnych opierają się na wynikach badan uzyskanych dla problemu częściowego pokrycia zbioru. Zostało udowodnione, ze biorąc pod uwagę pewne założenia dotyczące klasy NP, algorytm zachłanny pozwala uzyskać wyniki, bliskie wynikom uzyskiwanym przez najlepsze przybliżone wielomianowe algorytmy, dla minimalizacji długości częściowych reguł decyzyjnych oraz minimalizacji całkowitej wagi atrybutów
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Books on the topic "Rough Set Theory (RST)"

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Browne, Ciarán. Enhanced rough set theory. The Author], 1999.

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Akama, Seiki, Yasuo Kudo, and Tetsuya Murai. Topics in Rough Set Theory. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-29566-0.

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Inuiguchi, Masahiro, Shoji Hirano, and Shusaku Tsumoto, eds. Rough Set Theory and Granular Computing. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36473-3.

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Yang, Xibei, and Jingyu Yang. Incomplete Information System and Rough Set Theory. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25935-7.

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Abraham, Ajith, Rafael Falcón, and Rafael Bello, eds. Rough Set Theory: A True Landmark in Data Analysis. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89921-1.

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

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Peters, James F. Transactions on Rough Sets XIV. Springer Berlin Heidelberg, 2011.

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Lin, T. Y. Rough Sets and Data Mining: Analysis of Imprecise Data. Springer US, 1996.

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Yang, Xibei. Incomplete Information System and Rough Set Theory: Models and Attribute Reductions. Springer Berlin Heidelberg, 2012.

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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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Book chapters on the topic "Rough Set Theory (RST)"

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Chelly, Zeineb, and Zied Elouedi. "RST-DCA: A Dendritic Cell Algorithm Based on Rough Set Theory." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34487-9_58.

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Polkowski, Lech. "Set Theory." In Rough Sets. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1776-8_6.

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Raza, Muhammad Summair, and Usman Qamar. "Rough Set Theory." In Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9166-9_3.

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Akama, Seiki, Tetsuya Murai, and Yasuo Kudo. "Rough Set Theory." In Intelligent Systems Reference Library. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72691-5_2.

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Raza, Muhammad Summair, and Usman Qamar. "Rough Set Theory." In Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4965-1_3.

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Shekhar, Shashi, and Hui Xiong. "Rough Set Theory." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1144.

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Raza, Muhammad Summair, and Usman Qamar. "RST Source Code." In Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4965-1_8.

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Polkowski, Lech. "Rough Set Theory: An Introduction." In Rough Sets. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1776-8_1.

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Raza, Muhammad Summair, and Usman Qamar. "Advance Concepts in RST." In Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4965-1_4.

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Nowicki, Robert K. "Rough Set Theory Fundamentals." In Studies in Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03895-3_2.

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Conference papers on the topic "Rough Set Theory (RST)"

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Zimo, Chen, Feng Hailong, and Li Yajun. "A Front Face Design of New Energy Vehicles Based on Rough Set Theory and Backpropagation Neural Network." In 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005142.

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With the increasing awareness of environmental protection and prominent problem of traditional energy, new energy vehicles are an important choice to replace traditional oil-fueled vehicles. As an important part of new energy vehicles, the design of front face has an important impact on vehicles’ image, sales, and brand awareness. A front face’s modeling design process of new energy vehicles is proposed in this paper based on Kansei Engineering (KE)/ Rough Set Theory (RST)/ Backpropagation Neural Network (BPNN). Firstly, Kansei semantic analysis is carried out on the front face’s modeling of n
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Fu, Hao Cheng. "Supervaluationism and rough set theory." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6468591.

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Zhi Kong, Liqun Gao, Lifu Wang, and Yang Li. "Two operators in rough set theory." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434180.

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Nguyen, Vu Thanh, and Nguyen Thi Ly Sa. "Evaluating technologies based rough set theory." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580523.

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Yao, Y. Y. "Concept lattices in rough set theory." In IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04. IEEE, 2004. http://dx.doi.org/10.1109/nafips.2004.1337404.

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Thilagavathy, C., and R. Rajesh. "A note on rough set theory." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5942046.

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Kadam, Jitendra, Kirti Thakur, and Ashok Sapkal. "Noise Prediction Using Rough Set Theory." In 2014 International Conference on Devices, Circuits and Communications (ICDCCom). IEEE, 2014. http://dx.doi.org/10.1109/icdccom.2014.7024731.

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Chang, B., H. F. Hung, and C. C. Lo. "Supplier selection using rough set theory." In 2007 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2007. http://dx.doi.org/10.1109/ieem.2007.4419435.

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Csajbok, Zoltan. "Partial approximative set theory: A generalization of the rough set theory." In 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2010. http://dx.doi.org/10.1109/socpar.2010.5686424.

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"EFFICIENT OBJECT DETECTION ROBUST TO RST WITH MINIMAL SET OF EXAMPLES." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001083601790185.

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