A Preprocessing Approach for Class-Imbalanced Data Using SMOTE and Belief Function Theory

Fares Grina, Zied Elouedi, Eric Lefevre. A Preprocessing Approach for Class-Imbalanced Data Using SMOTE and Belief Function Theory. In Cesar Analide, Paulo Novais, David Camacho, Hujun Yin, editors, Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4-6, 2020, Proceedings, Part II. Volume 12490 of Lecture Notes in Computer Science, pages 3-11, Springer, 2020. [doi]

Abstract

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