SMOTEBoost: Improving Prediction of the Minority Class in Boosting

  • Nitesh V. Chawla
  • Aleksandar Lazarevic
  • Lawrence O. Hall
  • Kevin W. Bowyer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2838)


Many real world data mining applications involve learning from imbalanced data sets. Learning from data sets that contain very few instances of the minority (or interesting) class usually produces biased classifiers that have a higher predictive accuracy over the majority class(es), but poorer predictive accuracy over the minority class. SMOTE (Synthetic Minority Over-sampling TEchnique) is specifically designed for learning from imbalanced data sets. This paper presents a novel approach for learning from imbalanced data sets, based on a combination of the SMOTE algorithm and the boosting procedure. Unlike standard boosting where all misclassified examples are given equal weights, SMOTEBoost creates synthetic examples from the rare or minority class, thus indirectly changing the updating weights and compensating for skewed distributions. SMOTEBoost applied to several highly and moderately imbalanced data sets shows improvement in prediction performance on the minority class and overall improved F-values.


Intrusion Detection Minority Class Class Imbalance Weak Learner Network Intrusion Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nitesh V. Chawla
    • 1
  • Aleksandar Lazarevic
    • 2
  • Lawrence O. Hall
    • 3
  • Kevin W. Bowyer
    • 4
  1. 1.Business Analytic SolutionsCanadian Imperial Bank of Commerce (CIBC), BCE PlaceTorontoCanada
  2. 2.Department of Computer ScienceUniversity of MinnesotaMinneapolisUSA
  3. 3.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA
  4. 4.Department of Computer Science and EngineeringUniversity of Notre DameUSA

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