Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets

Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets

In many real world classification tasks, all data classes are not represented equally. This problem, known also as the curse of class imbalanced in data sets, has a potential impact in the training procedure of a classifier by learning a model that will be biased in favor of the majority class. In t...

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Bibliographic Details
Main Authors: Christos K. Aridas, Stamatis Karlos, Vasileios G. Kanas, Nikos Fazakis, Sotiris B. Kotsiantis
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Active selection
classification
naive bayes
imbalanced data
under-sampling
Online Access:https://ieeexplore.ieee.org/document/8939418/

Internet

https://ieeexplore.ieee.org/document/8939418/

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