Relevant bibliographies by topics / Classification probabiliste

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Classification probabiliste'

Author: Grafiati
Published: 12 October 2024

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Journal articles on the topic "Classification probabiliste"

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Mselati, Benoit. "Classification et représentation probabiliste des solutions positives d'une équation elliptique semi-linéaire." Comptes Rendus Mathematique 335, no. 9 (November 2002): 733–38. http://dx.doi.org/10.1016/s1631-073x(02)02557-8.

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Garbolino, Emmanuel, Patrice De Ruffray, Henry Brisse, and Gilles Grandjouan. "Les phytoclimats de France : classification probabiliste de 1874 bio-indicateurs du climat." Comptes Rendus Biologies 331, no. 11 (November 2008): 881–95. http://dx.doi.org/10.1016/j.crvi.2008.08.009.

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Stan, Emanuela, Camelia-Oana Muresan, Raluca Dumache, Veronica Ciocan, Stefania Ungureanu, Dan Costachescu, and Alexandra Enache. "Sex Estimation from Computed Tomography of Os Coxae—Validation of the Diagnose Sexuelle Probabiliste (DSP) Software in the Romanian Population." Applied Sciences 14, no. 10 (May 13, 2024): 4136. http://dx.doi.org/10.3390/app14104136.

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This study aimed to evaluate the DSP method’s applicability to Romania’s contemporary population and to assess the accuracy and reliability of variables derived from CT images. A total of 80 pelvic CT scans were analyzed. Participants ranged from 22 to 93 years, with a mean age of 59.51 ± 22.7 years. All variables measured from the CT scans were analyzed using DSP software. The study found that sex estimation was possible in 71.25% of cases overall, with varying rates between males (57.50%) and females (85%). Despite encountering undetermined specimens comprising 42.5% males and 15% females, only one misclassification occurred. Regarding accuracy, the overall rate remained notably high at 98.24%. All female specimens that could be estimated were correctly classified (100% accuracy), while for males, the accuracy rate was 95.65%. Undetermined cases were noted to potentially impact the accuracy of sex classification, underscoring the critical role of precision in forensic contexts. In conclusion, the study highlights the importance of accuracy in forensic sex estimation. It emphasizes the confidence with which DSP software can be utilized, if not the only method, at least as a preliminary or adjuvantly accurate technique for sex estimation in forensic anthropology.
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Gamboa, Luis Fernando. "Strategic Uses of Mobile Phones in the BoP: Some Examples in Latin American Countries." Lecturas de Economía, no. 71 (February 23, 2010): 209–34. http://dx.doi.org/10.17533/udea.le.n71a4820.

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El objetivo del trabajo es analizar el uso de un conjunto de estrategias para minimizar el gasto en telefonía móvil en una encuesta de telefonía móvil para personas de bajos ingresos en Argentina, Brasil, Colombia, México y Perú. La metodología empleada incluye dos etapas; primero, se evalúa cuáles son los determinantes del uso de cada estrategia mediante un modelo probabilístico y se encuentra que la edad y el nivel de escolaridad influyen positivamente en la probabilidad de usar las alternativas; segundo, se utiliza un modelo de Poisson para evaluar el número de estrategias utilizadas. Aunque los resultados difieren entre países, es común encontrar que los usuarios tienden a utilizar varias estrategias. Palabras clave: Telefonía móvil, pobreza, modelos de conteo. Clasificación JEL : D12, C35, L86 Abstrac: This paper studies the determinants of the use of different strategies by mobile-users for reducing their spending. This empirical exercise is done with a special survey focused in lowincome people from developing countries such as Argentina, Brazil, Colombia, Mexico, and Peru. Our methodology is the following. First, we evaluate the determinants of use of each strategy by means of a probabilistic model and we find that education level and age are important determinants of the use of alternatives. Second, we use a Poisson regression model to study the number of strategies used. Although our findings differ among countries, the use of more than one strategy is common in the sample. Keywords: Mobile Phones, Poverty, Count Data. JEL Classification: D12, C35, L86 Résumé: L'objectif de cet article est d'analyser l'utilisation d'un ensemble de stratégies visant diminuer les dépenses dans l'utilisation des téléphones portables, à partir d'un sondage fait chez les personnes à bas revenu en Argentine, Brésil, Colombie, Mexique et Pérou. La méthodologie employée considère deux étapes: Premièrement, il s'agit de déterminer les causes de l'utilisation de chaque stratégie à travers un modèle probabiliste, ce qui nous a permis de conclure que l'âge et leniveau de scolarité des personnes ont un impact positif sur la probabilité d'utiliser les stratégies. Deuxièmement, on utilise un modèle Poisson pour évaluer le nombre de stratégies utilisées. Même si les résultats diffèrent entre les pays considérés, nous trouvons que les usagers des portables ont une tendance à utiliser plusieurs stratégies. Mots clé: Téléphonie mobile, pauvreté, modèles de comptage. Classification JEL : D12, C35, L86
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Yerokhin, A. L., and O. V. Zolotukhin. "Fuzzy probabilistic neural network in document classification tasks." Information extraction and processing 2018, no. 46 (December 27, 2018): 68–71. http://dx.doi.org/10.15407/vidbir2018.46.068.

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Selianinau, Mikhail. "Podejście probabilistyczne do klasyfikacji cyfrowych obrazów twarzy." Prace Naukowe Akademii im. Jana Długosza w Częstochowie. Technika, Informatyka, Inżynieria Bezpieczeństwa 6 (2018): 563–74. http://dx.doi.org/10.16926/tiib.2018.06.40.

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Gouiouez, Mounir. "Probabilistic Graphical Model based on BablNet for Arabic Text Classification." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1241–50. http://dx.doi.org/10.5373/jardcs/v12sp7/20202224.

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Yang, Na, and Yongtao Zhang. "A Gaussian Process Classification and Target Recognition Algorithm for SAR Images." Scientific Programming 2022 (January 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/9212856.

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Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.
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Villa, Joe Luis, Ricard Boqué, and Joan Ferré. "Calculation of the probability of correct classification in probabilistic bagged k-Nearest Neighbours." Chemometrics and Intelligent Laboratory Systems 94, no. 1 (November 2008): 51–59. http://dx.doi.org/10.1016/j.chemolab.2008.06.007.

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Shi, Li Jun, Xian Cheng Mao, and Zheng Lin Peng. "Method for Classification of Remote Sensing Images Based on Multiple Classifiers Combination." Applied Mechanics and Materials 263-266 (December 2012): 2561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2561.

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This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coefficient was increased by 0.12 compared with SVM classification which has the highest accuracy in single sub-classifications.
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Dissertations / Theses on the topic "Classification probabiliste"

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Ambroise, Christophe. "Approche probabiliste en classification automatique et contraintes de voisinage." Compiègne, 1996. http://www.theses.fr/1996COMPD917.

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Ce travail propose de nouveaux algorithmes de classification pour résoudre des problèmes d'analyse de données où des contraintes naturelles apparaissent : respect d'une topologie (cartes de Kohonen), données spatiales. Les mélanges finis de lois gaussiennes et l'estimation de paramètres par l'algorithme EM constituent le cadre de ce mémoire. Le modèle des cartes topologiques de Kohonen introduisant la notion de contrainte, nous nous sommes intéressés à montrer les liens qui existent entre cette approche et les modèles de mélanges. Cette recherche a abouti au développement de variantes de l'algorithme EM ayant des comportements identiques à l'algorithme de Kohonen et possédant de bonnes propriétés de convergence. Dans le cas des données spatiales, l'a priori suivant est considéré : deux individus géographiquement proches ont plus de chance d'appartenir à une même classe que deux individus éloignés. Des algorithmes originaux, basés sur l'algorithme EM, sont proposés pour prendre en compte l'aspect spatial des données. Ces algorithmes peuvent être utilisés pour trouver une partition d'un ensemble d'individus localisés géographiquement, ce qui englobe la problématique de la segmentation d'image. Un parallèle entre les méthodes développées dans ce mémoire et les techniques markoviennes de segmentation bayésienne non supervisée d'image a été établi. Enfin, les méthodes présentées sont illustrées et comparées à l'aide d'applications concrètes
This thesis proposes new clustering algorithms well suited for data analysis problems where natural constraints appear: preservation of a topology, spatial data. Gaussian mixture models and the estimation of parameters by the EM algorithm constitute the background of the work. The Kohonen Map algorithm introduces the idea of constraint in clustering. We show the relationship between this neural approach and Gaussian mixture models. This leads us to propose a variant of the EM algorithm which has similar behaviour as the Kohonen algorithm and whose convergence is proven. When dealing with spatial data, we consider the following constraint: two objects which are neighbours are more likely to belong to the same class than two objects which are spatially far away. Original algorithms based on the EM algorithm are proposed for taking into account this spatial constraint. These algorithms may be used for seeking a partition of objects which have a geographical location. This encompasses the problem of unsupervised image segmentation. A theoretical link between our approach and Markov random field models is established. The proposed methods are compared and illustrated by means of applications based on real data
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Bzioui, Mohamed. "Classification croisée et modèle." Compiègne, 1999. http://www.theses.fr/1999COMP1226.

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Dans le cadre des tableaux de contingences, des tableaux binaires et des tableaux de mesures, la classification croisée est basée sur un critère métrique sans faire référence à un modèle probabiliste. Dans ce travail, nous proposons un modèle de mélange croisé afin d'apporter un éclairage aux critères métriques existants, d'en développer d'autres et de proposer une solution au problème des données manquantes. Cette étude est réalisée sous deux approches : approche classification et approche estimation. En outre, nous étudions l'influence du choix entre les deux hypothèses : proportions des composants du mélange égales aux proportions inconnues. Ainsi, différents algorithmes sont développés avec différentes variantes. Des simulations sont réalisées suivant les différentes situations en tenant compte à la fois de l'approche choisie avec les deux hypothèses et les paramètres du modèle.
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Touzani, Abderrahmane. "Classification automatique par détection des contours des modes des fonctions de densité de probabilité multivariables et étiquetage probabiliste." Grenoble 2 : ANRT, 1987. http://catalogue.bnf.fr/ark:/12148/cb37610380w.

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Aznag, Mustapha. "Modélisation thématique probabiliste des services web." Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4028.

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Les travaux sur la gestion des services web utilisent généralement des techniques du domaine de la recherche d'information, de l'extraction de données et de l'analyse linguistique. Alternativement, nous assistons à l'émergence de la modélisation thématique probabiliste utilisée initialement pour l'extraction de thèmes d'un corpus de documents. La contribution de cette thèse se situe à la frontière de la modélisation thématique et des services web. L'objectif principal de cette thèse est d'étudier et de proposer des algorithmes probabilistes pour modéliser la structure thématique des services web. Dans un premier temps, nous considérons une approche non supervisée pour répondre à différentes tâches telles que la découverte et le regroupement de services web. Ensuite, nous combinons la modélisation thématique avec l'analyse de concepts formels pour proposer une méthode de regroupement hiérarchique de services web. Cette méthode permet une nouvelle démarche de découverte interactive basée sur des opérateurs de généralisation et spécialisation des résultats obtenus. Enfin, nous proposons une méthode semi-supervisée pour l'annotation automatique de services web. Nous avons concrétisé nos propositions par un moteur de recherche en ligne appelé WS-Portal. Nous offrons alors différentes fonctions facilitant la gestion de services web, par exemple, la découverte et le regroupement de services web, la recommandation des tags, la surveillance des services, etc. Nous intégrons aussi différents paramètres tels que la disponibilité et la réputation de services web et plus généralement la qualité de service pour améliorer leur classement (la pertinence du résultat de recherche)
The works on web services management use generally the techniques of information retrieval, data mining and the linguistic analysis. Alternately, we attend the emergence of the probabilistic topic models originally developed and utilized for topics extraction and documents modeling. The contribution of this thesis meets the topics modeling and the web services management. The principal objective of this thesis is to study and propose probabilistic algorithms to model the thematic structure of web services. First, we consider an unsupervised approach to meet different tasks such as web services clustering and discovery. Then we combine the topics modeling with the formal concept analysis to propose a novel method for web services hierarchical clustering. This method allows a novel interactive discovery approach based on the specialization and generalization operators of retrieved results. Finally, we propose a semi-supervised method for automatic web service annotation (automatic tagging). We concretized our proposals by developing an on-line web services search engine called WS-Portal where we incorporate our research works to facilitate web service discovery task. Our WS-Portal contains 7063 providers, 115 sub-classes of category and 22236 web services crawled from the Internet. In WS- Portal, several technologies, i.e., web services clustering, tags recommendation, services rating and monitoring are employed to improve the effectiveness of web services discovery. We also integrate various parameters such as availability and reputation of web services and more generally the quality of service to improve their ranking and therefore the relevance of the search result
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Touzani, Abderrahmane. "Classification automatique par détection des contours des modes des fonctions de densité de probabilité multivariables et étiquetage probabiliste." Lille 1, 1987. http://www.theses.fr/1987LIL10058.

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Présentation des méthodes non paramétriques permettant de connaître la valeur de la fonction de densité en chacun des points d'une discrétisation de l'espace en hypercubes élémentaires. L'estimateur est mis en forme grâce à l'introduction d'un filtre de type médian multidimensionnel. Deux opérateurs différentiels sont introduits et appliqués aux fonctions de densité estimées et filtrées. Un algorithme d'extraction de contour séquentiel permet d'exploiter la réponse des opérateurs différentiels pour identifier les contours des modes. L'intérêt de l'approche présentée est démontre pour les problèmes de classification automatique non supervisée, tant sous l'hypothèse non paramétrique que paramétrique
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Bassolet, Cyr Gabin. "Approches connexionnistes du classement en Osiris : vers un classement probabiliste." Université Joseph Fourier (Grenoble), 1998. http://www.theses.fr/1998GRE10086.

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Le classement d'instance est une fonction importante des systèmes de représentation de connaissances. Il est présent dans les systèmes de représentation de connaissances centrée objet sous le nom de classification d'objet, dans les logiques terminologiques comme un cas particulier de la classification de concepts, et, de manière implicite, dans les systèmes à base de règles, où les faits inférés peuvent être interprétés comme l'appartenance à une classe. Nous étudions le classement d'instance en Osiris, un système de représentation de connaissances centrée objets où la notion de vue jouent un rôle central. Le classement d'instance consiste à déterminer les vues valides d'un objet, ainsi que ses vues potentielles et invalides lorsqu'il est incomplètement connu. Nous montrons une possibilité de traduction des règles de production en Osiris, explicitant ainsi la fonction de classement des systèmes experts. Les contraintes de domaine jouent un rôle privilégié en Osiris. Elles permettent de réaliser une partition du domaine de chaque attribut, partition qui se prolonge à l'espace des objets pour constituer l'espace de classement, dont les éléments sont appelés eq-classes. Tous les objets d'une eq-classe ont le même comportement vis-à-vis du classement. Nous étudions plusieurs architectures connexionnistes pour le classement en Osiris, en privilégiant la détermination complète des vues valides, invalides et potentielles lors du classement d'objets partiellement connus. Nous proposons une méthode pour le classement probabiliste, sous l'hypothèse d'indépendance des attributs. Pour cela, nous distinguons deux sous-ensembles d'Osiris où cette hypothèse peut être faite. Dans le cas général, l'approche proposée fournit un mécanisme homogène pour la détermination des vues valides, invalides et potentielles, sans valuation probabiliste de ces dernières. Enfin, nous évoquons les possibilités de prise en compte des dépendances pour le classement probabiliste.
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PRICE, DAVID. "Classification probabiliste par reseaux de neurones ; application a la reconnaissance de l'ecriture manuscrite." Paris 6, 1996. http://www.theses.fr/1996PA066344.

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Cette these decrit une contribution a la realisation d'un systeme automatique de reconnaissance des montants litteraux de cheques, en ecriture manuscrite cursive. Parmi les nombreuses phases de traitement de l'information necessaires pour leur reconnaissance, nous nous interessons uniquement, dans ce travail, a la phase de classification: notre objectif est de fournir, pour chaque caractere, une liste d'identifications possibles, par ordre de vraisemblance decroissante. Cette liste est ensuite traitee par un systeme superviseur qui, a partir de plusieurs informations differentes, prend une decision de reconnaissance du montant. Le probleme pose est donc essentiellement un probleme d'estimation de probabilite d'appartenance d'une lettre inconnue a une classe parmi plusieurs possibles. Jusqu'a present, les classifieurs a base de reseaux de neurones ont ete utilises essentiellement pour prendre une decision ; pourtant, les proprietes mathematiques fondamentales des reseaux de neurones en font d'excellents candidats pour effectuer une estimation de probabilites. Notre travail a donc porte sur la recherche de methodes permettant d'estimer, a l'aide de reseaux de neurones, en disposant d'une base d'apprentissage necessairement limitee, les probabilites a posteriori des classes. Dans un premier temps, nous etablissons une distinction entre, d'une part, les systemes permettant une estimation directe des probabilites a posteriori, et, d'autre part, les classifieurs bayesiens, qui necessitent l'estimation des densites conditionnelles. Dans ce dernier cas, nous proposons des solutions originales permettant notamment l'estimation des densites conditionnelles a partir de fonctions discriminantes. Puis, nous presentons la classification probabiliste (bayesienne ou non) a l'aide de reseaux de neurones. Nous presentons, en premier lieu, les approches qui utilisent les architectures classiques telles que les perceptrons multi-couche et les reseaux a fonctions radiales de base. Nous presentons ensuite une methode originale qui consiste a decomposer un probleme multi-classe en un ensemble de problemes a deux classes, et a determiner les probabilites a posteriori a partir de celles qui sont estimees par les classifieurs a deux classes. Cette approche represente une alternative aux architectures precedemment citees, qui presente l'avantage d'etre modulaire et rapide a mettre en uvre. Enfin, dans le cadre de notre probleme de reconnaissance de cheques, nous montrons les effets que peuvent avoir les differentes methodes deja evoquees, pour la reconnaissance des caracteres, ainsi que pour la reconnaissance des mots qui l'emploie. Nous nous sommes attaches alors plus particulierement a la pertinence des mesures de performances, ainsi qu'au choix de la fonction de cout
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Dong, Yuan. "Modélisation probabiliste de classifieurs d’ensemble pour des problèmes à deux classes." Thesis, Troyes, 2013. http://www.theses.fr/2013TROY0013/document.

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L'objectif de cette thèse est d'améliorer ou de préserver les performances d'un système décisionnel quand l’environnement peut impacter certains attributs de l'espace de représentation à un instant donné ou en fonction de la position géographique de l’observation. S'inspirant des méthodes d'ensemble, notre approche a consisté à prendre les décisions dans des sous-espaces de représentation résultant de projections de l'espace initial, espérant ainsi travailler dans des sous-espaces non impactés. La décision finale est alors prise par fusion des décisions individuelles. Dans ce contexte, trois méthodes de classification (one-class SVM, Kernel PCA et Kernel ECA) ont été testées en segmentation d'images texturées qui constitue un support applicatif parfaitement adéquat en raison des ruptures de modèle de texture aux frontières entre deux régions. Ensuite, nous avons proposé une nouvelle règle de fusion reposant sur un test du rapport de vraisemblance pour un ensemble de classifieurs indépendants. Par rapport au vote majoritaire, cette règle de fusion a montré de meilleures performances face à l'altération de l'espace de représentation. Enfin, nous avons établi un modèle conjoint pour l’ensemble des variables décisionnelles de Bernoulli corrélées associées aux décisions des classifieurs individuels. Cette modélisation doit permettre de lier les performances des classifieurs individuels à la performance de la règle de décision globale et d’étudier et de maîtriser l'impact des changements de l'espace initial sur la performance globale
The objective of this thesis is to improve or maintain the performance of a decision-making system when the environment can impact some attributes of the feature space at a given time or depending on the geographical location of the observation. Inspired by ensemble methods, our approach has been to make decisions in representation sub-spaces resulting of projections of the initial space, expecting that most of the subspaces are not impacted. The final decision is then made by fusing the individual decisions. In this context, three classification methods (one-class SVM, Kernel PCA and Kernel ECA) were tested on a textured images segmentation problem which is a perfectly adequate application support because of texture pattern changes at the border between two regions. Then, we proposed a new fusion rule based on a likelihood ratio test for a set of independent classifiers. Compared to the majority vote, this fusion rule showed better performance against the alteration of the performance space. Finally, we modeled the decision system using a joint model for all decisions based on the assumption that decisions of individual classifiers follow a correlated Bernoulli law. This model is intended to link the performance of individual classifiers to the performance of the overall decision rule and to investigate and control the impact of changes in the original space on the overall performance
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Mselati, Benoît. "Classification et représentation probabiliste des solutions positives de delta u = u2 dans un domaine." Paris 6, 2002. http://www.theses.fr/2002PA066496.

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Charon, Clara. "Classification probabiliste pour la prédiction et l'explication d'événements de santé défavorables et évitables en EHPAD." Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS200.pdf.

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L'EHPAD, établissement d'hébergement pour personnes âgées dépendantes, constitue une option à laquelle a recours une population nombreuse et croissante, lorsque pour diverses raisons, et notamment de santé, il n'est plus possible de vivre à domicile.Avec le développement des nouvelles technologies informatiques dans le domaine de la santé, un nombre croissant d'établissements de santé sont équipés de systèmes d'information regroupant les données administratives et médicales des patients ainsi que des informations sur les soins qui leur sont prodigués.Parmi ces systèmes, les dossiers médicaux électroniques (DME) émergent comme des outils essentiels, offrant un accès rapide et aisé aux informations des patients dans le but d'améliorer la qualité et la sécurité des soins.Dans ce travail, nous utilisons les données anonymisées des DME de NETSoins, un logiciel largement utilisé dans les EHPAD en France, afin de proposer et d'analyser des classifieurs capables de prédire plusieurs événements de santé défavorables chez les personnes âgées qui sont potentiellement modifiables par des interventions de santé appropriées.Notre démarche se concentre notamment sur l'utilisation de méthodes capables de fournir des explications, notamment les modèles graphiques probabilistes tels que les réseaux bayésiens.Après un prétraitement complexe pour adapter des données d'une base événementielle en données utilisables par un apprentissage statistique, tout en conservant leur cohérence médicale, nous avons développé une méthodologie d'apprentissage mise en œuvre dans trois expériences de classification probabiliste utilisant des réseaux bayésiens distincts, ciblant différents événements : le risque de survenue de la première escarre, le risque d'hospitalisation en urgence à l'entrée du résident en EHPAD, et le risque de fracture dans les premiers mois d'hébergement.Pour chaque cible, nous avons comparé les performances de notre classifieur de réseaux bayésiens selon divers critères avec d'autres méthodes de machine learning ainsi qu'avec les pratiques actuellement utilisées en EHPAD pour prédire ces risques. Nous avons aussi confronté les résultats des réseaux bayésiens à l'expertise clinique.Cette étude démontre la possibilité de prédire ces événements à partir des données déjà collectées en routine par les soignants, ouvrant ainsi la voie à de nouveaux outils de prédiction intégrables directement dans le logiciel déjà utilisé par ces professionnels
Nursing homes, which provide housing for dependent elderly people,are an option used by a large and growing population when, for a variety of reasons, including health, it is no longer possible for them to live at home.With the development of new information technologies in the health sector, an increasing number of health care facilities are equipped with information systems that group together administrative and medical data of patients as well as information on the care they receive. Among these systems, electronic health records (EHRs) have emerged as essential tools, providing quick and easy access to patient information in order to improve the quality and safety of care.We use the anonymized data of the EHRs from NETSoins, a software widely used in nursing homes in France, to propose and analyze classifiers capable of predicting several adverse health events in the elderly that are potentially modifiable by appropriate health interventions. Our approach focuses in particular on the use of methods that can provide explanations, such as probabilistic graphical models, including Bayesian networks.After a complex preprocessing step to adapt event-based data into data suitable for statistical learning while preserving their medical coherence, we have developed a learning method applied in three probabilistic classification experiments using Bayesian networks, targeting different events: the risk of occurrence of the first pressure ulcer, the risk of emergency hospitalization upon the resident's entry into the nursing home, and the risk of fracture in the first months of housing.For each target, we have compared the performance of our Bayesian network classifier according to various criteria with other machine learning methods as well as with the practices currently used in nursing homes to predict these risks. We have also compared the results of the Bayesian networks with clinical expertise.This study demonstrates the possibility of predicting these events from the data already collected in routine by caregivers, thus paving the way for new predictive tools that can be integrated directly into the software already used by these professionals
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Books on the topic "Classification probabiliste"

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Hack, Henri Robert George Kenneth. Slope stability probability classification: SSPC = Helling stabiliteit classificatie : SSPC. Delft: International Institute for Aerospace Survey and Earth Sciences, 1996.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. Iowa City, Iowa: American College Testing Program, 1993.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. Iowa City, Iowa: American College Testing Program, 1993.

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Spray, Judith A. Multiple-category classification using a sequential probability ratio test. Iowa City, Iowa: American College Testing Program, 1993.

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1967-, Meira Wagner, ed. Demand-driven associative classification. London: Springer, 2011.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. Iowa City, Iowa: ACT, Inc., 2000.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. Iowa City, Iowa: ACT, Inc., 2000.

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Lin, Chuan-Ju. Effects of item-selection criteria on classification testing with the sequential probability ratio test. Iowa City, Iowa: ACT, Inc., 2000.

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Baram, Yoram. Estimation and classification by sigmoids based on mutual information. [Washington, D.C: National Aeronautics and Space Administration, 1994.

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Classification Group of SIS. Meeting. Classification and data analysis: Theory and application : proceedings of the biannual meeting of the Classification Group of Societa Italia di Statistica (SIS), Pescara, July 3-4, 1997. Edited by Vichi Maurizio 1959- and Opitz Otto. New York: Springer, 1999.

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Book chapters on the topic "Classification probabiliste"

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Gower, John C., and Gavin J. S. Ross. "Non-probabilistic Classification." In Studies in Classification, Data Analysis, and Knowledge Organization, 21–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72253-0_3.

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Bock, Hans H. "Probability Models for Convex Clusters." In Classification and Knowledge Organization, 3–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59051-1_1.

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Fuhr, Norbert. "Representations, Models and Abstractions in Probabilistic Information Retrieval." In Information and Classification, 259–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-50974-2_26.

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Pompe, Uroš, and Igor Kononenko. "Probabilistic first-order classification." In Inductive Logic Programming, 235–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3540635149_52.

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Bernardo, José M. "Bayesian Linear Probabilistic Classification." In Statistical Decision Theory and Related Topics IV, 151–62. New York, NY: Springer New York, 1988. http://dx.doi.org/10.1007/978-1-4613-8768-8_19.

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Vovk, Vladimir, Alexander Gammerman, and Glenn Shafer. "Probabilistic Classification: Venn Predictors." In Algorithmic Learning in a Random World, 157–79. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06649-8_6.

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Bock, Hans H. "Probabilistic Aspects in Classification." In Studies in Classification, Data Analysis, and Knowledge Organization, 3–21. Tokyo: Springer Japan, 1998. http://dx.doi.org/10.1007/978-4-431-65950-1_1.

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Aggarwal, Charu C. "Classification: A Probabilistic View." In Probability and Statistics for Machine Learning, 353–91. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53282-5_8.

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Guo, Gongde, Hui Wang, David Bell, and Zhining Liao. "Contextual Probability-Based Classification." In Lecture Notes in Computer Science, 313–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30464-7_25.

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Godehardt, Erhard. "Probability Models of Classification." In Graphs as Structural Models, 97–114. Wiesbaden: Vieweg+Teubner Verlag, 1988. http://dx.doi.org/10.1007/978-3-322-96310-9_5.

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Conference papers on the topic "Classification probabiliste"

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Gorguluarslan, Recep M., and Seung-Kyum Choi. "Predicting Reliability of Structural Systems Using Classification Method." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13323.

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This research examines classification approaches for estimating the reliability of structural systems. To validate the accuracy and efficiency of the classification methods, a practical engineering problem; namely, a spider assembly of a washing machine, has been considered. For the spider assembly, fatigue life test, finite element analysis, physical experimentation, and a classification processes are conducted in order to establish the analytical certification of its current design. Specifically, the finite element analysis and fatigue life analysis are performed and their results are validated compared to physical experimental results. The classification process is developed to estimate the probability of failure of the spider assembly in terms of stress and fatigue life. The relationship between the random quantities and structural responses of the spider assembly is established using probabilistic neural network and the support vector machine classifiers. The performance margin of the spider assembly is fully identified based on the estimated failure probability and structural analysis results from the fatigue life analysis and classifications.
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Horte, T., R. Skjong, P. Friis-Hansen, A. P. Teixeira, and F. Viejo de Francisco. "Probabilistic Methods Applied To Structural Design And Rule Development." In Developments in Classification & International Regulation 2007. RINA, 2007. http://dx.doi.org/10.3940/rina.dcir.2007.07.

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Bailer-Jones, Coryn A. L., Kester W. Smith, and Coryn A. L. Bailer-Jones. "Finding rare objects and building pure samples: Probabilistic quasar classification with Gaia." In CLASSIFICATION AND DISCOVERY IN LARGE ASTRONOMICAL SURVEYS: Proceedings of the International Conference: “Classification and Discovery in Large Astronomical Surveys”. AIP, 2008. http://dx.doi.org/10.1063/1.3059079.

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Cardelli, Luca, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, and Matthew Wicker. "Statistical Guarantees for the Robustness of Bayesian Neural Networks." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/789.

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We introduce a probabilistic robustness measure for Bayesian Neural Networks (BNNs), defined as the probability that, given a test point, there exists a point within a bounded set such that the BNN prediction differs between the two. Such a measure can be used, for instance, to quantify the probability of the existence of adversarial examples. Building on statistical verification techniques for probabilistic models, we develop a framework that allows us to estimate probabilistic robustness for a BNN with statistical guarantees, i.e., with a priori error and confidence bounds. We provide experimental comparison for several approximate BNN inference techniques on image classification tasks associated to MNIST and a two-class subset of the GTSRB dataset. Our results enable quantification of uncertainty of BNN predictions in adversarial settings.
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Pereira, Rafael S., and Fabio Porto. "Deep Learning Application for Plant Classification on Unbalanced Training Set." In XIII Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/bresci.2019.10023.

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Deep learning models expect a reasonable amount of training instances to improve prediction quality. Moreover, in classification problems, the occurrence of an unbalanced distribution may lead to a biased model. In this paper, we investigate the problem of species classification from plant images, where some species have very few image samples. We explore reduced versions of imagenet Neural Network winners architecture to filter the space of candidate matches, under a target accuracy level. We show through experimental results using real unbalanced plant image datasets that our approach can lead to classifications within the 5 best positions with high probability.
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Pereira, Rafael S., and Fabio Porto. "Deep Learning Application for Plant Classification on Unbalanced Training Set." In XIII Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/bresci.2019.6304.

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Deep learning models expect a reasonable amount of training in- stances to improve prediction quality. Moreover, in classification problems, the occurrence of an unbalanced distribution may lead to a biased model. In this paper, we investigate the problem of species classification from plant images, where some species have very few image samples. We explore reduced versions of imagenet Neural Network winners architecture to filter the space of candi- date matches, under a target accuracy level. We show through experimental results using real unbalanced plant image datasets that our approach can lead to classifications within the 5 best positions with high probability.
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Albini, Emanuele, Antonio Rago, Pietro Baroni, and Francesca Toni. "Relation-Based Counterfactual Explanations for Bayesian Network Classifiers." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/63.

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We propose a general method for generating counterfactual explanations (CFXs) for a range of Bayesian Network Classifiers (BCs), e.g. single- or multi-label, binary or multidimensional. We focus on explanations built from relations of (critical and potential) influence between variables, indicating the reasons for classifications, rather than any probabilistic information. We show by means of a theoretical analysis of CFXs’ properties that they serve the purpose of indicating (potentially) pivotal factors in the classification process, whose absence would give rise to different classifications. We then prove empirically for various BCs that CFXs provide useful information in real world settings, e.g. when race plays a part in parole violation prediction, and show that they have inherent advantages over existing explanation methods in the literature.
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Косян, Рубен, Ruben Kosyan, Viacheslav Krylenko, and Viacheslav Krylenko. "DEVELOPMENT OF THE BASIC CRITERIA FOR RUSSIAN COASTS TYPIFICATION." In Managing risks to coastal regions and communities in a changing world. Academus Publishing, 2017. http://dx.doi.org/10.31519/conferencearticle_5b1b94080e4924.02334863.

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There are many types of coasts classifications that indicate main coastal features. As a rule, the "static" state of the coasts is considered regardless of their evolutionary features and ways to further transformation. Since the most part of the coastal zone studies aimed at ensuring of economic activity, it is clear that the classification of coast types should indicate total information required by the users. Accordingly, the coast classification should include the criterion, characterizing as dynamic features of the coast and the conditions and opportunities of economic activity. The coast classification, of course, should be based on geomorphological coast typification. Similar typification has been developed by leading scientists from Russia and can be used with minimal modifications. The authors propose to add to basic information (geomorphological type of coast) the evaluative part for each coast sector. It will include the estimation of the coast changes probability and the complexity of the coast stabilization for economic activity. This method will allow to assess the dynamics of specific coastal sections and the processes intensity and, as a result – the stability of the coastal area.
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Косян, Рубен, Ruben Kosyan, Viacheslav Krylenko, and Viacheslav Krylenko. "DEVELOPMENT OF THE BASIC CRITERIA FOR RUSSIAN COASTS TYPIFICATION." In Managing risks to coastal regions and communities in a changing world. Academus Publishing, 2017. http://dx.doi.org/10.21610/conferencearticle_58b431526b37b.

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There are many types of coasts classifications that indicate main coastal features. As a rule, the "static" state of the coasts is considered regardless of their evolutionary features and ways to further transformation. Since the most part of the coastal zone studies aimed at ensuring of economic activity, it is clear that the classification of coast types should indicate total information required by the users. Accordingly, the coast classification should include the criterion, characterizing as dynamic features of the coast and the conditions and opportunities of economic activity. The coast classification, of course, should be based on geomorphological coast typification. Similar typification has been developed by leading scientists from Russia and can be used with minimal modifications. The authors propose to add to basic information (geomorphological type of coast) the evaluative part for each coast sector. It will include the estimation of the coast changes probability and the complexity of the coast stabilization for economic activity. This method will allow to assess the dynamics of specific coastal sections and the processes intensity and, as a result – the stability of the coastal area.
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Wang, Eric, Pasha Khosravi, and Guy Van den Broeck. "Probabilistic Sufficient Explanations." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/424.

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Understanding the behavior of learned classifiers is an important task, and various black-box explanations, logical reasoning approaches, and model-specific methods have been proposed. In this paper, we introduce probabilistic sufficient explanations, which formulate explaining an instance of classification as choosing the "simplest" subset of features such that only observing those features is "sufficient" to explain the classification. That is, sufficient to give us strong probabilistic guarantees that the model will behave similarly when all features are observed under the data distribution. In addition, we leverage tractable probabilistic reasoning tools such as probabilistic circuits and expected predictions to design a scalable algorithm for finding the desired explanations while keeping the guarantees intact. Our experiments demonstrate the effectiveness of our algorithm in finding sufficient explanations, and showcase its advantages compared to Anchors and logical explanations.
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Reports on the topic "Classification probabiliste"

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Zeitouni, Ofer, and Sanjeev R. Kulkarni. A General Classification Rule for Probability Measures. Fort Belvoir, VA: Defense Technical Information Center, August 1993. http://dx.doi.org/10.21236/ada455893.

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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, May 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techniques for uncertainty representation; a description of the different steps involved in a Probabilistic Risk Assessment (PRA) or Quantitative Risk Assessment (QRA), and an analysis of the types of uncertainty that can affect each of these steps; annexes giving an overview of a number of tools used during probabilistic risk assessment, including the HAZID technique, fault trees and event tree analysis.
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SOHN, HOON, DAVID W. ALLEN, KEITH WORDEN, and CHARLES R. FARRAR. STATISTICAL DAMAGE CLASSIFICATION USING SEQUENTIAL PROBABILITY RATIO TESTS. Office of Scientific and Technical Information (OSTI), February 2002. http://dx.doi.org/10.2172/808089.

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Pouliot, D., R. Latifovic, and W. Parkinson. Influence of sample distribution and prior probability adjustment on land cover classification. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/297517.

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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33560.

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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced.
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de Dieu Niyigena, Jean, Innocent Ngaruye, Joseph Nzabanita, and Martin Singull. Approximation of misclassification probabilities using quadratic classifier for repeated measurements with known covariance matrices. Linköping University Electronic Press, August 2024. http://dx.doi.org/10.3384/lith-mat-r-2024-02.

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Quadratic discriminant analysis is a well-established supervised classification method, which extends the linear the linear discriminant analysis by relaxing the assumption of equal variances across classes. In this study, quadratic discriminant analysis is used to develop a quadratic classification rule based on repeated measurements. We employ a bilinear regression model to assign new observations to predefined populations and approximate the misclassification probability. Through weighted estimators, we estimate unknown mean parameters and derive moments of the quadratic classifier. We then conduct numerical simulations to compare misclassification probabilities using true and estimated mean parameters, as well as probabilities computed through simulation. Our findings suggest that as the distance between groups widens, the misclassification probability curve decreases, indicating that classifying observations is easier in widely separated groups compared to closely clustered ones.
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Eckert, Richard. PR-186-184509-R01 Guideline for Erosional Velocity. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2020. http://dx.doi.org/10.55274/r0011655.

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A guideline to determine erosional velocity limits for liquid hydrocarbon transmission pipelines was developed based on a multi-analytical probabilistic approach that integrated results from two erosional models: DNV GL RP-O501 and University of Tulsa SPPS v5.3. The guideline uses a simple classification tree model as first approach to provide conservative erosional velocities with a minimum amount of input data. The guideline also presents an alternative probabilistic approach for determining erosional velocities when the classification tree cannot be used, e.g., when there is too much data uncertainty or the erosional velocity limit (which is highly conservative) is lower than the current or expected liquid velocity in the pipeline. The database table is available for download. The link is https://www.prci.org/Research/DesignMaterialsConstruction/DMCProjects/FLOW-1-2/142187/181060.aspx This report has a related webinar.
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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a dataset that consists of thermal images containing buried objects paired with the corresponding environmental and meteorological conditions, we employ a machine learning approach to determine which environmental conditions are the most impactful on the visibility of the buried objects. We find the key environmental conditions include incoming shortwave solar radiation, soil volumetric water content, and average air temperature. For each image, ROIs are computed using a computer vision approach and these ROIs are coupled with the most important environmental conditions to form the input for the classification algorithm. The environmentally informed classification algorithm produces a decision on whether the ROI contains a buried object by simultaneously learning on the ROIs with a classification neural network and on the environmental data using a tabular neural network. On a given set of ROIs, we have shown that the environmentally informed classification approach improves the detection of buried objects within the ROIs.
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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.
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