Journal Article10.1016/J.NEUCOM.2017.03.011
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TL;DR: This paper introduces a fast, novel clustering-based undersampling technique for addressing binary-class imbalance problems, which demonstrates high predictive performance, while its time complexity is bound by the size of the minority class instances.
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About: This article is published in Neurocomputing. The article was published on 21 Jun 2017. The article focuses on the topics: Cluster analysis & Statistical classification.
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Citations
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Fuzzy-Based Information Decomposition for Incomplete and Imbalanced Data Learning
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Overlap-based undersampling for improving imbalanced data classification.
Pattaramon Vuttipittayamongkol,Eyad Elyan,Andrei Petrovski,Chrisina Jayne +3 more
- 09 Nov 2018
TL;DR: This paper proposes a new undersampling method that eliminates negative instances from the overlapping region and hence improves the visibility of the minority instances and shows statistically significant improvements in classification performance.
A novel progressively undersampling method based on the density peaks sequence for imbalanced data
TL;DR: Zhang et al. as discussed by the authors proposed a novel under-sampling method for imbalanced data, which exploits a sequence of density peaks to progressively extract instances from the majority classes of the imbalance data.
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References
SMOTE: synthetic minority over-sampling technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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SMOTE: Synthetic Minority Over-sampling Technique
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.