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. In this paper, we propose to simplify the computational phase and remove the dependence on observations, but more importantly to take into account the uncertainty already present in the labels, \emph{i.e.} the uncertainty of the oracles. Two strategies are proposed, sampling by Klir uncertainty, which addresses the exploration-exploitation problem, and sampling by evidential epistemic uncertainty, which extends the reducible uncertainty to the evidential framework, both using the theory of belief functions

Real bird dataset with imprecise and uncertain values

The theory of belief functions allows the fusion of imperfect data from different sources. Unfortunately, few real, imprecise and uncertain datasets exist to test approaches using belief functions. We have built real birds datasets thanks to the collection of numerous human contributions that we make available to the scientific community. The interest of our datasets is that they are made of human contributions, thus the information is therefore naturally uncertain and imprecise. These imperfections are given directly by the persons. This article presents the data and their collection through crowdsourcing and how to obtain belief functions from the data

Real bird dataset with imprecise and uncertain values

International audienceThe theory of belief functions allows the fusion of imperfect data from different sources. Unfortunately, few real, imprecise and uncertain datasets exist to test approaches using belief functions. We have built real birds datasets thanks to the collection of numerous human contributions that we make available to the scientific community. The interest of our datasets is that they are made of human contributions, thus the information is therefore naturally uncertain and imprecise. These imperfections are given directly by the persons. This article presents the data and their collection through crowdsourcing and how to obtain belief functions from the data

Measuring the Expertise of Workers for Crowdsourcing Applications

International audienceCrowdsourcing platforms enable companies to propose tasks to a large crowd of users. The workers receive a compensation for their work according to the serious of the tasks they managed to accomplish. The evaluation of the quality of responses obtained from the crowd remains one of the most important problems in this context. Several methods have been proposed to estimate the expertise level of crowd workers. We propose an innovative measure of expertise assuming that we possess a dataset with an objective comparison of the items concerned. Our method is based on the definition of four factors with the theory of belief functions. We compare our method to the Fagin distance on a dataset from a real experiment, where users have to assess the quality of some audio recordings. Then, we propose to fuse both the Fagin distance and our expertise measure

The Forest Ecosystems Observatory in Guadeloupe (FWI)

International audienceBetween 2010 and 2012, Parc National de la Guadeloupe, Office National des Forêts, and Université des Antilles et de la Guyane established 9 permanent 1-ha plots in tropical rain forest of Basse-Terre Island (Guadeloupe). These plots comprise the Guadeloupian Forest Observatory, and are specifically designed for long-term tree-growth measurements and forest-dynamics surveys. We marked more than 8000 trees with a diameter at breast height >10 cm and equipped them with tape dendrometers for measurement at 5-y intervals. We describe our field protocols for plot establishment and tree-growth data collection, and present preliminary results from analyses of the first data recorded in these plots

Modeling evolutionary responses in crowdsourcing MCQ using belief function theory

International audienceCrowdsourcing is a widespread method for outsourcing tasks to a crowd of contributors. These simple tasks are often formulated by multiple choice questionaries (MCQs) to which the contributor has to give a precise answer. We hypothesize that offering the contributor to fill in an imperfect answer and to evolve it is more profitable than a single precise answer. In this paper, we propose a model for the evolutionary answers of contributors to MCQs in crowdsourcing platforms. In order to realize this modeling we use the theory of belief functions. The model and experiments conducted on real data from crowdsourcing campaigns are presented in this paper. Our experiments show that modelling evolutionary responses using consonant mass functions improves the quality of the results obtained when aggregating responses compared with majority voting

Modélisation du profil des contributeurs dans les plateformes de crowdsourcing

International audienceThe crowdsourcing consists in the externalisation of tasks to a crowd of people remunerated to execute this ones. The crowd, usually diversified, can include users without qualification and/or motivation for the tasks. In this paper we will introduce a new method of user expertise modelization in the crowdsourcing platforms based on the theory of belief functions in order to identify serious and qualificated users.Le crowdsourcing consiste a l'externalisation de tâches à une foule de contributeurs rémunérés pour les effectuer. La foule, généralement très diversifiée, peut inclure des contributeurs non-qualifiés pour la tâche et/ou non-sérieux. Nous présentons ici une nouvelle méthode de modélisation de l'expertise du contributeur dans les plateformes de crowdsourcing se fondant sur la théorie des fonctions de croyance afin d'identifier les contributeurs sérieux et qualifiés

A mean distance between elements of same class for rich labels

International audienceThe prevalence of imperfections in data, characterized by uncertainty and imprecision, prompts the need for effective modeling techniques. The theory of belief functions offers a mathematical framework to address this challenge. In this paper, we tackle the problem of calculating the mean distance between elements of the same class, especially when class membership is uncertain and imprecise. Leveraging belief functions and a notion of similarity between elements, we propose a solution and validate its efficacy through experimental evaluations. The proposed method proves effective when labels exhibit low imprecision, whereas unsupervised methods may be more effective for labels closer to complete ignorance