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Academic literature on the topic 'Relative Indiscernibility Relation'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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Journal articles on the topic "Relative Indiscernibility Relation"

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Li, Xu, Jianguo Tang, Bing Hu, and Yi Li. "Indiscernibility and Discernibility Relations Attribute Reduction with Variable Precision." Scientific Programming 2022 (April 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/5077968.

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Attribute reduction is a popular topic in research on rough sets. In the classical model, much progress has been made in the study of the attribute reduction of indiscernibility and discernibility relations. To enhance the fault tolerance of the model, concepts of both indiscernibility and discernibility relations involving uncertain or imprecise information are proposed in this paper. The attribute reductions of the relative β -indiscernibility relation and relative β -discernibility relation and their algorithms are proposed. When the precision satisfies certain conditions, the reduction of two relation concepts can be converted into a positive region reduction. Therefore, the discernibility matrix is used to construct the reductions of the two relation concepts and the positive region. Furthermore, the corresponding algorithm of the relative β -indiscernibility (discernibility) relation reduction can be optimized when the precision is greater than 0.5, and this is used to develop an optimization algorithm that constructs the discernibility matrix more efficiently. Experiments show the feasibility of the two relation reduction algorithms. More importantly, the reduction algorithms of the two relations and the optimization algorithm are compared to demonstrate the feasibility of the optimization algorithm proposed in this paper.

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HU, QINGHUA, and DAREN YU. "ENTROPIES OF FUZZY INDISCERNIBILITY RELATION AND ITS OPERATIONS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 12, no. 05 (2004): 575–89. http://dx.doi.org/10.1142/s0218488504003089.

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Yager's entropy was proposed to compute the information of fuzzy indiscernibility relation. In this paper we present a novel interpretation of Yager's entropy in discernibility power of a relation point of view. Then some basic definitions in Shannon's information theory are generalized based on Yager's entropy. We introduce joint entropy, conditional entropy, mutual information and relative entropy to compute the information changes for fuzzy indiscerniblity relation operations. Conditional entropy and relative conditional entropy are proposed to measure the information increment, which is interpreted as the significance of an attribute in fuzzy rough set model. As an application, we redefine independency of an attribute set, reduct, relative reduct in fuzzy rough set model based on Yager's entropy. Some experimental results show the proposed approach is suitable for fuzzy and numeric data reduction.

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Hirano, Shoji, and Shusaku Tsumoto. "An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations -Rough Clustering-." Journal of Advanced Computational Intelligence and Intelligent Informatics 7, no. 2 (2003): 169–77. http://dx.doi.org/10.20965/jaciii.2003.p0169.

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This paper presents a new indiscernibility-based clustering method called rough clustering, that can handle relative proximity. Relative proximity is a class of proximity measures that can be used to represent subjective similarity or dissimilarity; such as human judgment about likeness of persons. Since relative proximity is not necessarily required to satisfy the triangular inequality, conventional centroid-based clustering methods may fail to produce good clusters due to inappropriate assignment of cluster representatives. Our method is based on iterative refinement of N binary classifications, where N denotes the number of objects. First, an equivalence relation, that classifies all the other objects into two classes, similar and dissimilar, is assigned by referring to their relative proximity. Next, for each pair of the objects, we count the number of binary classifications in which the pair is included in the same class. We call this number as indiscernibility degree. If the indiscernibility degree of a pair is larger than a user-defined threshold value, we modify the equivalence relations so that all of them commonly classify the pair into the same class. This process is repeated until class assignment becomes stable. Consequently, we obtain the clustering result that follows given level of granularity without using geometric measures.

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Kumar, Kamesh. "Assistant Tools for Medical Diagnostics through Rough Set-Based Data Analysis." Indian Journal Of Science And Technology 17, no. 31 (2024): 3174–82. http://dx.doi.org/10.17485/ijst/v17i31.1987.

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Objective: This study aims to identify and prioritize critical symptoms of pneumonia, determining their relative importance. Based on these findings, a decision rule base is developed to enhance efficiency of pneumonia diagnosis. Methods: A disease may concern with a set of symptoms, also same set of symptoms may appear in different diseases. To make the diagnostic decision apparent, it is advantageous to identify and assigning extra importance to some critical symptoms. We applied the reduction of attributes and importance of attributes indices of rough set theory to characterize the critical symptoms or core symptoms. Thereafter, an algorithm has been proposed to set up assistant tools for medical diagnosis. Findings: The reduction of attributes and importance of attribute, indices are calculated for conditional attributes namely respiratory rate (RR), cough level (CO), chest in drawing (CI) and temperature (T) concerned to the decision attribute (disease) pneumonia. The findings reveal insights into identifying the critical symptoms and also its degree of likelihood of importance. Chest in drawing and temperature are found in the list of core symptoms. Also, degree of Importance for these symptoms are computed as 9/14 and 1/21, respectively. A rule base is optimized to assist the diagnostic process of disease pneumonia effectively. Novelty: This research contributes in presenting a novel mathematical algorithm that identifies the critical symptoms and irrelevant symptoms for pneumonia disease. This aids the diagnostic process by redefining the decision rule base. The numerical computation provides a practical and visual tool to assess potential outcomes of the proposed technique. Applications: The proposed idea can assist medical experts in better and robust diagnostics when quantity of symptoms is increased and linguistically expressed that creates non-specificity type incompleteness. Keywords: Rough Set, Lower and Upper Approximations, Indiscernibility Relation, Reduction of Attribute, Importance of Attribute, Medical Diagnosis

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Kamesh, Kumar. "Assistant Tools for Medical Diagnostics through Rough Set-Based Data Analysis." Indian Journal of Science and Technology 17, no. 31 (2024): 3174–82. https://doi.org/10.17485/IJST/v17i31.1987.

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Abstract <strong>Objective:</strong> This study aims to identify and prioritize critical symptoms of pneumonia, determining their relative importance. Based on these findings, a decision rule base is developed to enhance efficiency of pneumonia diagnosis.<strong> Methods:</strong> A disease may concern with a set of symptoms, also same set of symptoms may appear in different diseases. To make the diagnostic decision apparent, it is advantageous to identify and assigning extra importance to some critical symptoms. We applied the reduction of attributes and importance of attributes indices of rough set theory to characterize the critical symptoms or core symptoms. Thereafter, an algorithm has been proposed to set up assistant tools for medical diagnosis.<strong> Findings:</strong> The reduction of attributes and importance of attribute, indices are calculated for conditional attributes namely respiratory rate (RR), cough level (CO), chest in drawing (CI) and temperature (T) concerned to the decision attribute (disease) pneumonia. The findings reveal insights into identifying the critical symptoms and also its degree of likelihood of importance. Chest in drawing and temperature are found in the list of core symptoms. Also, degree of Importance for these symptoms are computed as 9/14 and 1/21, respectively. A rule base is optimized to assist the diagnostic process of disease pneumonia effectively. <strong>Novelty:</strong> This research contributes in presenting a novel mathematical algorithm that identifies the critical symptoms and irrelevant symptoms for pneumonia disease. This aids the diagnostic process by redefining the decision rule base. The numerical computation provides a practical and visual tool to assess potential outcomes of the proposed technique. Applications: The proposed idea can assist medical experts in better and robust diagnostics when quantity of symptoms is increased and linguistically expressed that creates non-specificity type incompleteness. <strong>Keywords:</strong> Rough Set, Lower and Upper Approximations, Indiscernibility Relation, Reduction of Attribute, Importance of Attribute, Medical Diagnosis

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Shampa, Sengupta, and Kr. Das Asit. "Single Reduct Generation Based on Relative Indiscernibility of Rough Set Theory." February 29, 2012. https://doi.org/10.5281/zenodo.8123681.

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In real world everything is an object which represents particular classes. Every object can be fully described by its attributes. Any real world dataset contains large number of attributes and objects. Classifiers give poor performance when these huge datasets are given as input to it for proper classification. So from these huge dataset most useful attributes need to be extracted that contribute the maximum to the decision. In the paper, attribute set is reduced by generating reducts using the indiscernibility relation of Rough Set Theory (RST). The method measures similarity among the attributes using relative indiscernibility relation and computes attribute similarity set. Then the set is minimized and an attribute similarity table is constructed from which attribute similar to maximum number of attributes is selected so that the resultant minimum set of selected attributes (called reduct) cover all attributes of the attribute similarity table. The method has been applied on glass dataset collected from the UCI repository and the classification accuracy is calculated by various classifiers. The result shows the efficiency of the proposed method. 

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Shampa, Sengupta, and Kr. Das Asit. "SINGLE REDUCT GENERATION BASED ON RELATIVE INDISCERNIBILITY OF ROUGH SET THEORY." February 29, 2012. https://doi.org/10.5281/zenodo.3374443.

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In real world everything is an object which represents particular classes. Every object can be fully described by its attributes. Any real world dataset contains large number of attributes and objects. Classifiers give poor performance when these huge datasets are given as input to it for proper classification. So from these huge dataset most useful attributes need to be extracted that contribute the maximum to the decision. In the paper, attribute set is reduced by generating reducts using the indiscernibility relation of Rough Set Theory (RST). The method measures similarity among the attributes using relative indiscernibility relation and computes attribute similarity set. Then the set is minimized and an attribute similarity table is constructed from which attribute similar to maximum number of attributes is selected so that the resultant minimum set of selected attributes (called reduct) cover all attributes of the attribute similarity table. The method has been applied on glass dataset collected from the UCI repository and the classification accuracy is calculated by various classifiers. The result shows the efficiency of the proposed method.

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Dissertations / Theses on the topic "Relative Indiscernibility Relation"

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Tasdelen, Iskender. "Resemblance: A Logico-philosophical Analysis." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606719/index.pdf.

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Indiscernibility, interchangeability and resemblance relations may be named as weak-identity relations or identity-like relations. Indeed,identical objects can be thought to satisfy all these three relations. According to realist view, these relations are defined on the basis of properties of objects. While indiscernibility is a context-independent relation, since indiscernibility is defined in terms of all properties of objects, interchangeability and resemblance relations are defined in terms of some significant properties of objects and thus they should be regarded as context-dependent. We should consider the following points while comparing rivalling theories of resemblance: 1. The theory explaining resemblance relations between objects should cover as many domains as possible. Instead of simply admitting that objects of a given type may resemble each other, the theory should explain how these relations are possible. 2. Objects do not just resemble or they do not<br>resemblance is a relation that admits degrees. Thus the theory providing a finer analysis of close and weak resemblances should be preferred. In comparison to resemblance nominalism, realist theory of resemblance is strongerwith respect to both points. The criticism that realist view may not explain degrees of resemblance can be rejoined by removing the indeterminacy as to the nature of this notion.Among knowledge representation systems, property and attribute systems provide a simple but strong models for analytic ontology. Weak identity relations can easily be defined in these systems and the results following from these definitions can be seen to conform to our intuitions about weak identity relations.

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Books on the topic "Relative Indiscernibility Relation"

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Button, Tim, and Sean Walsh. Indiscernibility. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198790396.003.0015.

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This chapter explores Leibniz's principle of the Identity of Indiscernibles. Model theory supplies us with the resources to distinguish between many different notions of indiscernibility; we can vary: (a) the primitive ideology (b) the background logic and (c) the grade of discernibility. We use these distinctions to discuss the possibility of singling-out “indiscernibles”. And we then use these to distinctions to explicate Leibniz's famous principle. While model theory allows us to make this principle precise, the sheer number of different precise versions of this principle made available by model theory can serve to mitigate some of the initial excitement of this principle. We round out the chapter with two technical topics: indiscernibility in infinitary logic, and the relation between indiscernibility, orders, and stability.

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Gardner, Colin. Louis Malle’s Kleistian War Machine: Becoming-Animal, Becoming-Woman, Becoming-Imperceptible in Black Moon (1975). Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474422734.003.0005.

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Framed through an analysis of Kleist’s molecular war machine in his play, Penthesilea, in which Achilles and Penthesilea form a new assemblage of affective war, this chapter explores Louis Malle’s Black Moon (1975) where the battle of the sexes becomes the catalyst for a new series of becomings. The film takes the form of a waking dream as a teenage fugitive, Lily is led through a series of depersonalized movements by a unicorn to a secluded Dordogne farm where Kleist’s utopian “mad duality” is manifested though a strange, non-Oedipal family dynamic in which a mute brother and his sheep-herding sister – both also called Lily – live with a group of naked children and a bedridden elderly woman whose companion is a talking rat and where the animals are treated as equal agencies in the narrative. Although by film’s end Brother and Sister Lily become caught up in the ravages of a gender war, teenage Lily inherits this ‘deterritorialized velocity of affect’ by adopting the role of the breastfeeding mother to the unicorn, all in relation to the becoming multiplicity of the pack: in short, a true war machine that envelops both protagonists and spectators alike in a transformed zone of indiscernibility.

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hoogland, renée c. Un/Becoming Claude Cahun: Zigzagging in a Pack. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474422734.003.0008.

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Considered odd, obscene, a genius nonetheless, at the time she created her best-known works, French photographer and writer Claude Cahun (1894-1950) cuts a particularly unruly figure in literary criticism and art history. Her recalcitrant faux autobiography Aveux non avenus, [Disavowals, or, Cancelled Confessions] (1930), a book of essays and recorded dreams illustrated with photomontages, have encouraged the artist’s association with High Modernism and Surrealism while her photographic self-portraits have been claimed for an affirmative (feminist) gender politics. However, the proliferous and mercurial nature of Cahun’s disavowed confessions and self-stagings defy easy “domestication.” Instead she constructs a continuously shifting configuration of fragments and collages: assemblages of singularities that are always in a multiplicity, in a pack. Escaping dominant forms of expression, Cahun’s work has nothing to do with recognition or imitation, nor does it constitute a relation of representation. The chapter argues instead that Cahun presents us in both her writing and in her photographic work with the successful experience of becoming in the absence of any final term or form. A becoming-animal that moves beyond destruction into the zone of indiscernibility where a work, or, perhaps, an oeuvre comes into view—an oeuvre that nonetheless remains decidedly outlandish.

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Book chapters on the topic "Relative Indiscernibility Relation"

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Peters, James F., and Maciej Borkowski. "K-means Indiscernibility Relation over Pixels." In Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25929-9_71.

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Lau, Simon Shek Yuen. "Image Segmentation Based on the Indiscernibility Relation." In Rough Sets, Fuzzy Sets and Knowledge Discovery. Springer London, 1994. http://dx.doi.org/10.1007/978-1-4471-3238-7_46.

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Wei, Qianjin, Tianlong Gu, Fengying Li, and Guoyong Cai. "The Representation of Indiscernibility Relation Using ZBDDs." In Intelligent Information Processing VI. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32891-6_28.

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Yang, Xibei, and Jingyu Yang. "Indiscernibility Relation, Rough Sets and Information System." In Incomplete Information System and Rough Set Theory. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25935-7_1.

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Grzymała-Busse, Jerzy W. "Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation." In Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25929-9_29.

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Grzymala-Busse, Jerzy W. "Characteristic Relations for Incomplete Data: A Generalization of the Indiscernibility Relation." In Transactions on Rough Sets IV. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11574798_4.

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Hirano, Shoji, and Shusaku Tsumoto. "Dealing with Relative Similarity in Clustering: An Indiscernibility Based Approach." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36175-8_51.

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Grzymala-Busse, Jerzy W. "Incomplete Data and Generalization of Indiscernibility Relation, Definability, and Approximations." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11548669_26.

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Sengupta, Shampa, and Asit Kr Das. "Single Reduct Generation by Attribute Similarity Measurement Based on Relative Indiscernibility." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27308-7_51.

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Nakata, Michinori, Norio Saito, Hiroshi Sakai, and Takeshi Fujiwara. "Kryszkiewicz’s Relation for Indiscernibility of Objects in Data Tables Containing Missing Values." In Rough Sets. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50959-9_12.

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Conference papers on the topic "Relative Indiscernibility Relation"

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Baowei Song, Baowei Zhang, and Chunxue Wei. "Retrieving classification rules based on indiscernibility relation." In 2010 International Conference On Computer and Communication Technologies in Agriculture Engineering (CCTAE). IEEE, 2010. http://dx.doi.org/10.1109/cctae.2010.5543257.

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Ognjenovic, Visnja, Vladimir Brtka, Martin Jovanovic, Eleonora Brtka, and Ivana Berkovic. "The representation of indiscernibility relation by graph." In 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY 2011). IEEE, 2011. http://dx.doi.org/10.1109/sisy.2011.6034298.

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Luo, Junfang, and Keyun Qin. "The Rough Set Model Based on Grade Indiscernibility Relation." In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE, 2015. http://dx.doi.org/10.1109/cit/iucc/dasc/picom.2015.203.

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GrzymalaBusse, Jerzy W., and Wojciech Rzasa. "Definability of Approximations for a Generalization of the Indiscernibility Relation." In 2007 IEEE Symposium on Foundations of Computational Intelligence. IEEE, 2007. http://dx.doi.org/10.1109/foci.2007.372149.

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Lu, Xiaoyong, Liqing Li, and Xiaogang Zhou. "Reduction Method of Order-Cycle Column Remove Based on Indiscernibility Relation." In 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE, 2009. http://dx.doi.org/10.1109/ncm.2009.213.

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Brtka, Vladimir, Ivana Berkovic, Edith Stokic, and Biljana Srdic. "A Comparison of Rule Sets Generated from Databases by Indiscernibility Relation - A Rough Sets Approach." In IEEE 3rd International Conference on Intelligent Computer Communication and Processing, ICCP 2007. IEEE, 2007. http://dx.doi.org/10.1109/iccp.2007.4352177.

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