Advances in Knowledge Discovery and Management
Volume 10
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Article
Evidential uncertainty sampling strategies for active learning
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In this paper, the aim is to simpli...
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Article
Open AccessThe OPS-SAT case: A data-centric competition for onboard satellite image classification
While novel artificial intelligence and machine learning techniques are evolving and disrupting established terrestrial technologies at an unprecedented speed, their adaptation onboard satellites is seemingly ...
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Book
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Article
Open AccessEvolutionary dynamics of genome size and content during the adaptive radiation of Heliconiini butterflies
Heliconius butterflies, a speciose genus of Müllerian mimics, represent a classic example of an adaptive radiation that includes a range of derived dietary, life history, physiological and neural traits. However,...
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Chapter and Conference Paper
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 bui...
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Chapter and Conference Paper
Belief Functions on Ordered Frames of Discernment
Most questionnaires offer ordered responses whose order is poorly studied via belief functions. In this paper, we study the consequences of a frame of discernment consisting of ordered elements on belief funct...
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Chapter and Conference Paper
Reliability-Based Imbalanced Data Classification with Dempster-Shafer Theory
The classification analysis of imbalanced data remains a challenging task since the base classifier usually focuses on the majority class and ignores the minority class. This paper proposes a reliability-based...
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Chapter and Conference Paper
Imperfect Labels with Belief Functions for Active Learning
Classification is used to predict classes by extracting information from labeled data. But sometimes the collected data is imperfect, as in crowdsourcing where users have partial knowledge and may answer with ...
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Chapter and Conference Paper
Validation of Smets’ Hypothesis in the Crowdsourcing Environment
In the late 1990s, Philippe Smets hypothesizes that the more imprecise humans are, the more certain they are. The modeling of human responses by belief functions has been little discussed. In this context, it ...
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Chapter and Conference Paper
Credal Clustering for Imbalanced Data
Traditional evidential clustering tends to build clusters where the number of data for each cluster fairly close to each other. However, it may not be suitable for imbalanced data. This paper proposes a new me...
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Chapter and Conference Paper
Unequal Singleton Pair Distance for Evidential Preference Clustering
Evidential preference based on belief function theory has been proposed recently, simultaneously characterizing preference information with uncertainty and imprecision. However, traditional distances on belief...
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Chapter and Conference Paper
Evidential Clustering Based on Transfer Learning
Clustering is an essential part of data mining, which can be used to organize data into sensible groups. Among the various clustering algorithms, the prototype-based methods have been most popularly applied du...
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Chapter and Conference Paper
Fast Unfolding of Credal Partitions in Evidential Clustering
Evidential clustering, based on the notion of credal partition, has been successfully applied in many fields, reflecting its broad appeal and usefulness as one of the steps in exploratory data analysis. However, ...
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Article
Evidential positive opinion influence measures for viral marketing
The viral marketing is a relatively new form of marketing that exploits social networks to promote a brand, a product, etc. The idea behind it is to find a set of influencers on the network that can trigger a ...
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Chapter and Conference Paper
Life and Death of Data in Data Lakes: Preserving Data Usability and Responsible Governance
Data crossing seeks the extraction of novel knowledge through correlations and dependencies among heterogeneous data, and is considered a key process in sustainable science to push back the current frontiers ...
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Chapter
Measuring the Expertise of Workers for Crowdsourcing Applications
platforms enable to propose tasks to a large crowd users. The workers receive a compensation for their work according to the serious of the they managed to accomplish. The evaluation of the of respons...
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Chapter
Conflict Management in Information Fusion with Belief Functions
In information fusion, the conflict is an important concept. Indeed, combining several imperfect experts or sources allows conflict. In the theory of belief functions, this notion has been discussed a lot. The...
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Book and Conference Proceedings
Belief Functions: Theory and Applications
5th International Conference, BELIEF 2018, Compiègne, France, September 17-21, 2018, Proceedings
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Chapter and Conference Paper
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. In order to find credal community structure in graph data sets, in this paper, we propose a n...
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Chapter and Conference Paper
Evidential Independence Maximization on Twitter Network
Detecting independent users in online social networks is an interesting research issue. In fact, independent users cannot generally be influenced, they are independent in their choices and decisions. Independe...
