SMOTE

SMOTE: Synthetic Minority Over-sampling Technique. An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ”normal” examples with only a small percentage of ”abnormal” or ”interesting” examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.


References in zbMATH (referenced in 163 articles , 1 standard article )

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  7. Mojiri, Arezou; Khalili, Abbas; Hamadani, Ali Zeinal: New hard-thresholding rules based on data splitting in high-dimensional imbalanced classification (2022)
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  9. Seitshiro, M. B.; Mashele, H. P.: Quantification of model risk that is caused by model misspecification (2022)
  10. Welchowski, Thomas; Maloney, Kelly O.; Mitchell, Richard; Schmid, Matthias: Techniques to improve ecological interpretability of black-box machine learning models. Case study on biological health of streams in the United States with gradient boosted trees (2022)
  11. Yuxiao Huang, Yan Ma: CIGAN: A Python Package for Handling Class Imbalance using Generative Adversarial Networks (2022) arXiv
  12. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
  13. Aminian, Ehsan; Ribeiro, Rita P.; Gama, João: Chebyshev approaches for imbalanced data streams regression models (2021)
  14. Barella, Victor H.; Garcia, Luís P. F.; de Souto, Marcilio C. P.; Lorena, Ana C.; de Carvalho, André C. P. L. F.: Assessing the data complexity of imbalanced datasets (2021)
  15. Bej, Saptarshi; Davtyan, Narek; Wolfien, Markus; Nassar, Mariam; Wolkenhauer, Olaf: LoRAS: an oversampling approach for imbalanced datasets (2021)
  16. Bernardo, Alessio; Della Valle, Emanuele: VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams (2021)
  17. Bulavas, Viktoras; Marcinkevičius, Virginijus; Rumiński, Jacek: Study of multi-class classification algorithms’ performance on highly imbalanced network intrusion datasets (2021)
  18. Cao, Yi; Liu, Xiaoquan; Zhai, Jia: Option valuation under no-arbitrage constraints with neural networks (2021)
  19. Chen, Baiyun; Xia, Shuyin; Chen, Zizhong; Wang, Binggui; Wang, Guoyin: RSMOTE: a self-adaptive robust SMOTE for imbalanced problems with label noise (2021)
  20. Chen, Zhi; Duan, Jiang; Kang, Li; Qiu, Guoping: A hybrid data-level ensemble to enable learning from highly imbalanced dataset (2021)

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