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Found 41 papers, 0 papers with code
Evidential uncertainties on rich labels for active learning
Recent research in active learning, and more precisely in uncertainty sampling, has focused on the decomposition of model uncertainty into reducible and irreducible uncertainties.
Estimation of the qualification and behavior of a contributor and aggregation of his answers in a crowdsourcing context
To overcome this problem, we propose a method, MONITOR, which estimates the contributor's profile and aggregates the collected data by taking into account their possible imperfections thanks to the theory of belief functions.
Belief functions on ordered frames of discernment
In this paper, we study the consequences of a frame of discernment consisting of ordered elements on belief functions.
Modelisation de l'incertitude et de l'imprecision de donnees de crowdsourcing : MONITOR
Crowdsourcing is defined as the outsourcing of tasks to a crowd of contributors.
Evidential positive opinion influence measures for viral marketing
We also use an influence maximization model that the set of detected influencers for each scenario.
Contributors profile modelization in crowdsourcing platforms
The crowdsourcing consists in the externalisation of tasks to a crowd of people remunerated to execute this ones.
Evidential community detection based on density peaks
Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set.
Conflict management in information fusion with belief functions
We recall in this chapter some of them and we propose a discussion to consider the conflict in information fusion with the theory of belief functions.
An automatic water detection approach based on Dempster-Shafer theory for multi spectral images
Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification.
Preference fusion and Condorcet's Paradox under uncertainty
The aim of this work is to propose a preference fusion method that copes with uncertainty and escape from the Condorcet paradox.
The Advantage of Evidential Attributes in Social Networks
In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step.
Evidence combination for a large number of sources
It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement.
A reliability-based approach for influence maximization using the evidence theory
Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network.
Dynamic time warping distance for message propagation classification in Twitter
In this paper, we are mainly interested in the classification of social messages based on their spreading on online social networks (OSN).
Two Evidential Data Based Models for Influence Maximization in Twitter
In this paper, we propose two evidential influence maximization models for Twitter social network.
Social and Information Networks
Une mesure d'expertise pour le crowdsourcing
The evaluation of the participants work quality is a major issue in crowdsourcing.
Maximizing positive opinion influence using an evidential approach
In this paper, we propose a new data based model for influence maximization in online social networks.
Characterization of experts in crowdsourcing platforms
We model such partial or incomplete responses with the help of belief functions, and we derive a measure that characterizes the expertise level of each participant.
Semi-supervised evidential label propagation algorithm for graph data
In the task of community detection, there often exists some useful prior information.
Evidential Label Propagation Algorithm for Graphs
With the increasing size of social networks in real world, community detection approaches should be fast and accurate.
ECMdd: Evidential c-medoids clustering with multiple prototypes
In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is utilized to represent one class.
The belief noisy-or model applied to network reliability analysis
In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed.
Adaptive imputation of missing values for incomplete pattern classification
We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory.
Evidential relational clustering using medoids
Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets.
Combining partially independent belief functions
The theory of belief functions manages uncertainty and also proposes a set of combination rules to aggregate opinions of several sources.
A Distance-Based Decision in the Credal Level
Second, we give experiments showing that our rule is able to decide on a set of hypotheses.
Inclusion within Continuous Belief Functions
Defining and modeling the relation of inclusion between continuous belief function may be considered as an important operation in order to study their behaviors.
Uncertainty in Ontology Matching: A Decision Rule-Based Approach
Considering the high heterogeneity of the ontologies pub-lished on the web, ontology matching is a crucial issue whose aim is to establish links between an entity of a source ontology and one or several entities from a target ontology.
Classification of Message Spreading in a Heterogeneous Social Network
We tested our classifier on a real word network that we collected from Twitter, and our experiments show the performance of our belief classifier.
Second-Order Belief Hidden Markov Models
The probabilistic HMMs have been one of the most used techniques based on the Bayesian model.
Int{é}gration d'une mesure d'ind{é}pendance pour la fusion d'informations
Many information sources are considered into data fusion in order to improve the decision in terms of uncertainty and imprecision.
Features modeling with an $α$-stable distribution: Application to pattern recognition based on continuous belief functions
The aim of this paper is to show the interest in fitting features with an $\alpha$-stable distribution to classify imperfect data.
Belief Hidden Markov Model for speech recognition
Speech Recognition searches to predict the spoken words automatically.
Trolls Identification within an Uncertain Framework
Based on the assumption consisting on the trolls' integration in the successful discussion threads, we try to detect the presence of such malicious users.
Designing a Belief Function-Based Accessibility Indicator to Improve Web Browsing for Disabled People
The purpose of this study is to provide an accessibility measure of web-pages, in order to draw disabled users to the pages that have been designed to be ac-cessible to them.
Belief Approach for Social Networks
In this paper, we tried to model a social network as being a network of fusion of information and determine the true nature of the received message in a well-defined node by proposing a new model: the belief social network.
Consid{é}rant la d{é}pendance dans la th{é}orie des fonctions de croyance
In this paper, we propose to learn sources independence in order to choose the appropriate type of combination rules when aggregating their beliefs.
Belief Hierarchical Clustering
In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases.
Median evidential c-means algorithm and its application to community detection
In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed.
Evidential-EM Algorithm Applied to Progressively Censored Observations
Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data.
