Готові списки джерел за темами / Possibilistic similarity

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Добірка наукової літератури з теми "Possibilistic similarity"

Автор: Grafiati
Опубліковано: 8 лютого 2025

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Статті в журналах з теми "Possibilistic similarity"

1

SGARRO, ANDREA. "UTILITIES AND DISTORTIONS: AN OBJECTIVE APPROACH TO POSSIBILITIES CODING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 13, no. 02 (April 2005): 139–61. http://dx.doi.org/10.1142/s0218488505003369.

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Анотація:
We re-take the possibilistic (as opposed to probabilistic) approach to information coding put forward in 1,2. To enhance the possibilistic approach also outside the realm of "subjective" uncertainties, in this paper we adopt an "objective" interpretation of possibilistic source coding based on utility functions and an "objective" interpretation of possibilistic channel coding based on distortion measures and similarity indices. We stress the relationship between possibilistic coding as based on distortions between sequences and algebraic coding as based on minimum distances between codewords. We compute the operational (coding-theoretic) entropy for a new class of possibilistic sources.
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2

Singh, Abhishek, and Anil Kumar. "Introduction of Local Spatial Constraints and Local Similarity Estimation in Possibilistic c-Means Algorithm for Remotely Sensed Imagery." Journal of Modeling and Optimization 11, no. 1 (June 15, 2019): 51–56. http://dx.doi.org/10.32732/jmo.2019.11.1.51.

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Анотація:
This paper presents a unique Possibilistic c-Means with constraints (PCM-S) with Adaptive Possibilistic Local Information c-Means (ADPLICM) in a supervised way by incorporating local information through local spatial constraints and local similarity measures in Possibilistic c-Means Algorithm. PCM-S with ADPLICM overcome the limitations of the known Possibilistic c-Means (PCM) and Possibilistic c-Means with constraints (PCM-S) algorithms. The major contribution of proposed algorithm to ensure the noise resistance in the presence of random salt & pepper noise. The effectiveness of proposed algorithm has been analysed on random “salt and pepper” noise added on original dataset and Root Mean Square Error (RMSE) has been calculated between original dataset and noisy dataset. It has been observed that PCM-S with ADPLICM is effective in minimizing noise during supervised classification by introducing local convolution.
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3

Miyamoto, Sadaaki, Youhei Kuroda, and Kenta Arai. "Algorithms for Sequential Extraction of Clusters by Possibilistic Method and Comparison with Mountain Clustering." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (September 20, 2008): 448–53. http://dx.doi.org/10.20965/jaciii.2008.p0448.

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Анотація:
In addition to fuzzy c-means, possibilistic clustering is useful because it is robust against noise in data. The generated clusters are, however, strongly dependent on an initial value. We propose a family of algorithms for sequentially generating clusters “one cluster at a time,” which includes possibilistic medoid clustering. These algorithms automatically determine the number of clusters. Due to possibilistic clustering's similarity to the mountain clustering by Yager and Filev, we compare their formulation and performance in numerical examples.
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4

Juarez, Jose M., Francisco Guil, Jose Palma, and Roque Marin. "Temporal similarity by measuring possibilistic uncertainty in CBR." Fuzzy Sets and Systems 160, no. 2 (January 2009): 214–30. http://dx.doi.org/10.1016/j.fss.2008.05.017.

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5

Yu yu Liao, Ke xin Jia, and Zi shu He. "Similarity Measure based Robust Possibilistic C-means Clustering Algorithms." Journal of Convergence Information Technology 6, no. 12 (December 31, 2011): 129–38. http://dx.doi.org/10.4156/jcit.vol6.issue12.17.

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6

Charfi, Amal, Sonda Ammar Bouhamed, Eloi Bosse, Imene Khanfir Kallel, Wassim Bouchaala, Basel Solaiman, and Nabil Derbel. "Possibilistic Similarity Measures for Data Science and Machine Learning Applications." IEEE Access 8 (2020): 49198–211. http://dx.doi.org/10.1109/access.2020.2979553.

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7

Bai, Xiangzhi, Yuxuan Zhang, Haonan Liu, and Zhiguo Chen. "Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation." IEEE Transactions on Cybernetics 49, no. 7 (July 2019): 2618–30. http://dx.doi.org/10.1109/tcyb.2018.2830977.

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8

Alsahwa, B., B. Solaiman, É. Bossé, S. Almouahed, and D. Guériot. "A Method of Spatial Unmixing Based on Possibilistic Similarity in Soft Pattern Classification." Fuzzy Information and Engineering 8, no. 3 (September 2016): 295–314. http://dx.doi.org/10.1016/j.fiae.2016.11.004.

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9

Devi, R. "Unsupervised Kernel-Induced Fuzzy Possibilistic C-Means Technique in Investigating Real-World Data." Journal of Physics: Conference Series 2199, no. 1 (February 1, 2022): 012033. http://dx.doi.org/10.1088/1742-6596/2199/1/012033.

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Анотація:
Abstract The goal of this study is to break down a large dataset into meaningful groupings. Due to the vast dimension and significant resemblance seen among data, exploring divided clusters in real-world datasets is the most difficult assignment. As a result, this work proposes a fuzzy set-based unsupervised effective clustering technique that includes possibilistic memberships, and fuzzy membership degrees into the membership, weighted Cauchy kernel-based similarity measure and center equations. The empirical findings demonstrate the feasibility of the proposed effective clustering technique.
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10

Schockaert, Steven, and Henri Prade. "An Inconsistency-Tolerant Approach to Information Merging Based on Proposition Relaxation." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 363–68. http://dx.doi.org/10.1609/aaai.v24i1.7583.

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Анотація:
Inconsistencies between different information sources may arise because of statements that are inaccurate, albeit not completely false. In such scenarios, the most natural way to restore consistency is often to interpret assertions in a more flexible way, i.e. to enlarge (or relax) their meaning. As this process inherently requires extra-logical information about the meaning of atoms, extensions of classical merging operators are needed. In this paper, we introduce syntactic merging operators, based on possibilistic logic, which employ background knowledge about the similarity of atomic propositions to appropriately relax propositional statements.
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Більше джерел

Дисертації з теми "Possibilistic similarity"

1

Jenhani, Ilyes. "From possibilistic similarity measures to possibilistic decision trees." Thesis, Artois, 2010. http://www.theses.fr/2010ARTO0402/document.

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Анотація:
Cette thèse traite deux problèmes importants dans les domaine de l'apprentissage automatique et du raisonnement dans l'incertain : comment évaluer une relation de similarité entre deux informations incertaines et comment assurer la classification \`a partir de données incertaines. Notre première principale contribution est de proposer une approche, appelée arbre de décision possibiliste, qui permet de construire des arbres de décision à partir de données d'apprentissage imparfaites. Plus précisément, elle traite des données caractérisées par des classes incertaines o\`u l'incertitude est modélisée avec la théorie des possibilités quantitative. Nous avons développé trois approches d'arbres de décision possibilistes. Pour chacune des approches, nous avons été confrontés à résoudre plusieurs problèmes pour pouvoir construire des arbres de décision possibilistes, tels que, comment définir une mesure de sélection d'attributs quand les classes sont représentes par des distributions de possibilité, comment trouver les critères d'arrêt et comment les feuilles vont être étiquetées dans ce contexte incertain. La première approche, appelée arbre de décision possibiliste basée sur la non- spécificité, utilise le concept de non-spécificité relatif à la théorie des possibilités dans la définition de sa mesure de sélection d'attributs. Cette approche maintient les distributions de possibilité durant toutes les étapes de la procédure de construction et ce particulièrement, au moment de l'évaluation de la quantité d'information apportée par chaque attribut. En revanche, la deuxième et la troisième approches, appelées arbre de décision possibiliste basé sur la similarité et arbre de décision possibiliste basé sur le clustering, éliminent automatiquement les distributions de possibilité dans leurs mesures de sélection d'attributs. Cette stratégie a permis d'étendre le ratio de gain et, par conséquent, d'étendre l'algorithme C4.5 pour qu'il puisse traiter des données libellées par des classes possibilistes. Ces deux dernières approches sont principalement basées sur le concept de similarité entre les distributions de possibilité étudié dans la thèse.La deuxième principale contribution de cette thèse concerne l'analyse des mesures de similarité en théorie des possibilités. En effet, un challenge important était de fournir une analyse des mesures de similarité possibiliste conduite par les propriétés qu'elles doivent satisfaire. Après avoir montré le rôle important de la notion d'incohérence dans l'évaluation de la similarité en théorie des possibilités, une nouvelle mesure, appelée affinité de l'information a été proposée. Cette mesure satisfait plusieurs propriétés que nous avons établies. A la fin de cette thèse, nous avons proposé des expérimentations pour comparer et montrer la faisabilité des approches d'arbres de décision possibilistes que nous avons développées
This thesis concerns two important issues in machine learning and reasoning under uncertainty: how to evaluate a similarity relation between two uncertain pieces of information, and how to perform classification from uncertain data. Our first main contribution is to propose a so-called possibilistic decision tree which allows to induce decision trees from training data afflicted with imperfection. More precisely, it handles training data characterized by uncertain class labels where uncertainty is modeled within the quantitative possibility theory framework. We have developed three possibilistic decision tree approaches. For each approach, we were faced and solved typical questions for inducing possibilistic decision trees such as how to define an attribute selection measure when classes are represented by possibility distributions, how to find the stopping criteria and how leaves should be labeled in such uncertain context. The first approach, so-called, non-specificity-based possibilistic decision tree uses the concept of non-specificity relative to possibility theory in its attribute selection measure component. This approach keeps up the possibility distributions within all the stages of the building procedure and especially when evaluating the informativeness of the attributes in the attribute selection step. Conversely, the second and the third approaches, so-called similarity-based possibilistic decision tree and clustering-based possibilistic decision tree, automatically, get rid of possibility distributions in their attribute selection measure. This strategy has allowed them to extend the gain ratio criterion and hence to extend the C4.5 algorithm to handle possibilistic labeled data. These two possibilistic decision tree approaches are mainly based on the concept of similarity between possibility distributions.This latter issue constitutes our second main contribution in this thesis. In fact, an important challenge was to provide a property-based analysis of possibilistic similarity measures. After showing the important role that inconsistency could play in assessing possibilistic similarity, a new inconsistency-based possibilistic similarity measure, so-called information affinity has been proposed. This measure satisfies a set of natural properties that we have established. Finally, we have conducted experiments to show the feasibility and to compare the different possibilistic decision tree approaches developed in this thesis
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2

Ben, marzouka Wided. "Modélisation conjointe des connaissances humaines et machines pour de meilleurs approches d’aide à la décision." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0452.

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Анотація:
Ce travail de recherche propose une nouvelle approche de modélisation des connaissances humaines et machines dans le processus d’identification des pannes (IP). Trois contributions majeures sont apportées : (1) la structuration de la base de cas hypothétiques (H-CB), qui reflète le raisonnement séquentiel des experts et permet une meilleure organisation des observations pour optimiser l’IP ; (2) l’extraction des connaissances, intégrant un raisonnement possibiliste pour traiter l’hétérogénéité des primitives observées, introduire un vecteur de connaissance possibiliste (PSK) et améliorer la précision du diagnostic grâce à une similarité possibiliste ; et (3) un apprentissage incrémental qui favorise l’intégration continue des nouvelles connaissances, garantissant ainsi l’évolution du système en fonction des nouveaux cas rencontrés et une amélioration constante des décisions. L’approche développée, Possibilistic Hypothetical Case-Based Reasoning (PH-CBR), a été validée expérimentalement. Les résultats démontrent une amélioration significative par rapport aux approches classiques de raisonnement à base de cas, réduisant le nombre de primitives nécessaires pour l’IP et optimisant l’efficacité des machines industrielles en minimisant le temps de diagnostic
This research introduces a novel approach to modeling human and machine knowledge in the fault identification (FI). It makes three key contributions:(1) the structuring of the Hypothetical Case Base (H-CB), which captures the sequential reasoning of experts and organizes observations to enhance FI; (2) knowledge extraction, incorporating possibilistic reasoning to manage the heterogeneity of observed primitives, introduce a Possibilistic Knowledge Vector (PSK), and improve diagnostic accuracy through possibilistic similarity; and (3) an incremental learning mechanism that enables the continuous integration of new knowledge, allowing the system to evolve with newly encountered cases and refine decision-making. The proposed approach, Possibilistic Hypothetical Case-Based Reasoning (PH-CBR), has been experimentally validated. Results show significant improvements over traditional case-based reasoning methods, reducing the number of primitives required for FI and enhancing industrial machine efficiency by minimizing diagnostic time
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Частини книг з теми "Possibilistic similarity"

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Solaiman, Basel, and Éloi Bossé. "Possibilistic Similarity Measures." In Possibility Theory for the Design of Information Fusion Systems, 83–135. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32853-5_4.

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2

Jenhani, Ilyes, Salem Benferhat, and Zied Elouedi. "Possibilistic Similarity Measures." In Foundations of Reasoning under Uncertainty, 99–123. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10728-3_6.

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3

Dahabiah, Anas, John Puentes, and Basel Solaiman. "Possibilistic Similarity Estimation and Visualization." In Lecture Notes in Computer Science, 273–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04417-5_26.

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4

Tanaka, Hideo, and Peijun Guo. "Possibilistic Data Analysis and Its Similarity to Rough Sets." In Data Mining, Rough Sets and Granular Computing, 518–36. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1791-1_26.

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5

Jenhani, Ilyes, Nahla Ben Amor, Zied Elouedi, Salem Benferhat, and Khaled Mellouli. "Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information." In Lecture Notes in Computer Science, 840–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75256-1_73.

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6

Jia, Kexin, Miao He, and Ting Cheng. "A New Similarity Measure Based Robust Possibilistic C-Means Clustering Algorithm." In Web Information Systems and Mining, 335–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23982-3_41.

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7

Slokom, Manel, and Raouia Ayachi. "A Hybrid User and Item Based Collaborative Filtering Approach by Possibilistic Similarity Fusion." In Advances in Combining Intelligent Methods, 125–47. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46200-4_7.

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Тези доповідей конференцій з теми "Possibilistic similarity"

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"Possibilistic Similarity based Image Classification." In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004265002710275.

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Zhang, Chunhui, Yiming Zhou, and Trevor Martin. "Similarity Based Fuzzy and Possibilistic c-means Algorithm." In 11th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2008. http://dx.doi.org/10.2991/jcis.2008.9.

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Skrjanc, Igor, Araceli Sanchis de Miguel, Jose Antonio Iglesias, Agapito Ledezma, and Dejan Dovzan. "Evolving Cauchy possibilistic clustering based on cosine similarity for monitoring cyber systems." In 2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2017. http://dx.doi.org/10.1109/eais.2017.7954825.

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"A Method of Pixel Unmixing by Classes based on the Possibilistic Similarity." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004826202200226.

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