Titlebook: Belief Functions: Theory and Applications; 6th International Co Thierry Denœux,Eric Lefèvre,Frédéric Pichon Conference proceedings 2021 Spr

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Conference proceedings 2021er 2021. The 30 full papers presented in this book were carefully selected and reviewed from 37 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.

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https://doi.org/10.1007/978-1-349-86189-7e steps in exploratory data analysis. However, it is time-consuming due to the introduction of meta-cluster, which is regarded as a new cluster and defined by the disjunction (union) of several special (singleton) clusters. In this paper, a simple and fast method is proposed to extract the credal pa

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https://doi.org/10.1007/978-1-349-86189-7 suitable for imbalanced data. This paper proposes a new method, called credal clustering (CClu), to deal with imbalanced data based on the theory of belief functions. Consider a dataset with . wanted classes, the credal .-means (CCM) clustering method is employed at first to divide the dataset into

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Religion and Materialist Philosophycult to learn an ideal cluster model. In such cases, multi-view data can be taken into consideration in the clustering task. However, the inconsistency cross views may increase the cluster uncertainty. In this research, a new clustering method for multi-view object data, called MvWECM (Multi-view We

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Aktuelle Situation der Kreditwirtschaft,y and imprecision. However, traditional distances on belief functions do not adapt to some intrinsic properties of preference relations, especially when indifference relation is taken into comparison, therefore may cause inconsistent results in preference-based applications. In order to solve this i

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Grundlagen und Struktur des Modells,a powerful clustering algorithm developed in the theoretical framework of belief functions. Based on the concept of credal partition, it extends those of hard, fuzzy, and possibilistic clustering algorithms. However, as a clustering algorithm, it can only work well when the data is sufficient and th

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Belief Functions: Theory and Applications978-3-030-88601-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/183301.jpg

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Conference proceedings 2021er 2021. The 30 full papers presented in this book were carefully selected and reviewed from 37 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data