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 132 articles , 1 standard article )

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  1. Akalin, Altuna: Computational genomics with R. With the assistance of Verdan Franke, Bora Uyar and Jonathan Ronen (2021)
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  3. Chaabane, Ikram; Guermazi, Radhouane; Hammami, Mohamed: Enhancing techniques for learning decision trees from imbalanced data (2020)
  4. Gubela, Robin M.; Lessmann, Stefan; Jaroszewicz, Szymon: Response transformation and profit decomposition for revenue uplift modeling (2020)
  5. Halbersberg, Dan; Wienreb, Maydan; Lerner, Boaz: Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier (2020)
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  7. Mahajan, Pravar Dilip; Maurya, Abhinav; Megahed, Aly; Elwany, Alaa; Strong, Ray; Blomberg, Jeanette: Optimizing predictive precision in imbalanced datasets for actionable revenue change prediction (2020)
  8. Ruehle, Fabian: Data science applications to string theory (2020)
  9. Sun, Hongwei; Cui, Yuehua; Gao, Qian; Wang, Tong: Trimmed LASSO regression estimator for binary response data (2020)
  10. Tao, Xinmin; Li, Qing; Guo, Wenjie; Ren, Chao; He, Qing; Liu, Rui; Zou, JunRong: Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering (2020)
  11. Tsukuda, Koji; Mano, Shuhei; Yamamoto, Toshimichi: Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset (2020)
  12. Wu, Di; Zhang, Jiangjiang; Geng, Shaojin; Cai, Xingjuan; Zhang, Guoyou: A multi-objective bat algorithm for software defect prediction (2020)
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  18. Park, Soyoung; Carriquiry, Alicia: Learning algorithms to evaluate forensic glass evidence (2019)
  19. Poterie, A.; Dupuy, J.-F.; Monbet, V.; Rouvière, L.: Classification tree algorithm for grouped variables (2019)
  20. Razzaghi, Talayeh; Safro, Ilya; Ewing, Joseph; Sadrfaridpour, Ehsan; Scott, John D.: Predictive models for bariatric surgery risks with imbalanced medical datasets (2019)

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