Recent research in active learning, and more precisely in uncertainty sampling, has focused on the decomposition of model uncertainty into reducible and irreducible uncertainties.
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.
The crowdsourcing consists in the externalisation of tasks to a crowd of people remunerated to execute this ones.
Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification.
In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step.
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.
In this paper, we are mainly interested in the classification of social messages based on their spreading on online social networks (OSN).
In this paper, we propose two evidential influence maximization models for Twitter social network.
Social and Information Networks
In this paper, we propose a new data based model for influence maximization in online social networks.
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.
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.
We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory.
Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets.
Defining and modeling the relation of inclusion between continuous belief function may be considered as an important operation in order to study their behaviors.
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.
We tested our classifier on a real word network that we collected from Twitter, and our experiments show the performance of our belief classifier.
The aim of this paper is to show the interest in fitting features with an $\alpha$-stable distribution to classify imperfect data.
Based on the assumption consisting on the trolls' integration in the successful discussion threads, we try to detect the presence of such malicious users.
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.
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.
In this paper, we propose to learn sources independence in order to choose the appropriate type of combination rules when aggregating their beliefs.
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 (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data.