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 141 articles , 1 standard article )

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  1. Yuan, Bo; Liu, Wenhuang: Measure oriented training: a targeted approach to imbalanced classification problems (2012) ioport
  2. Zhou, Tianyi; Tao, Dacheng; Wu, Xindong: Compressed labeling on distilled labelsets for multi-label learning (2012)
  3. Bowyer, Kevin W.; Chawla, Nitesh V.; Hall, Lawrence O.; Kegelmeyer, W. Philip: SMOTE: synthetic minority over-sampling technique (2011) ioport
  4. Fernández-Navarro, Francisco; Hervás-Martínez, César; Gutiérrez, Pedro Antonio: A dynamic over-sampling procedure based on sensitivity for multi-class problems (2011)
  5. Jiang, Yuan; Li, Ming; Zhou, Zhi-Hua: Software defect detection with Rocus (2011) ioport
  6. Luengo, Julián; Fernández, Alberto; García, Salvador; Herrera, Francisco: Addressing data complexity for imbalanced data sets: Analysis of SMOTE-based oversampling and evolutionary undersampling (2011) ioport
  7. Shoemaker, Larry; Banfield, Robert E.; Hall, Lawrence O.; Bowyer, Kevin W.; Kegelmeyer, W. Philip: Detecting and ordering salient regions (2011)
  8. Soda, Paolo: A multi-objective optimisation approach for class imbalance learning (2011)
  9. Zhao, Zhuangyuan; Zhong, Ping; Zhao, Yaohong: Learning SVM with weighted maximum margin criterion for classification of imbalanced data (2011)
  10. Ducange, Pietro; Lazzerini, Beatrice; Marcelloni, Francesco: Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets (2010) ioport
  11. Fernández, Alberto; Del Jesus, María José; Herrera, Francisco: On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets (2010) ioport
  12. He, Jingrui; Carbonell, Jaime: Coselection of features and instances for unsupervised rare category analysis (2010)
  13. Qu, Hai-Ni; Li, Guo-Zheng; Xu, Wei-Sheng: An asymmetric classifier based on partial least squares (2010)
  14. Villar, Pedro; Fernández, Alberto; Herrera, Francisco: A genetic algorithm for feature selection and granularity learning in fuzzy rule-based classification systems for highly imbalanced data-sets (2010)
  15. Wang, Benjamin X.; Japkowicz, Nathalie: Boosting support vector machines for imbalanced data sets (2010) ioport
  16. Wu, Junjie; Xiong, Hui; Chen, Jian: COG: local decomposition for rare class analysis (2010) ioport
  17. Castro, Cristiano Leite; Carvalho, Mateus Araujo; Braga, Antônio Padua: An improved algorithm for SVMs classification of imbalanced data sets (2009)
  18. Fernández, Alberto; José del Jesus, María; Herrera, Francisco: Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets (2009)
  19. Lan, Jyh-shyan; Berardi, Victor L.; Patuwo, B. Eddy; Hu, Michael: A joint investigation of misclassification treatments and imbalanced datasets on neural network performance (2009) ioport
  20. Moskovitch, Robert; Stopel, Dima; Feher, Clint; Nissim, Nir; Japkowicz, Nathalie; Elovici, Yuval: Unknown malcode detection and the imbalance problem (2009) ioport