Applied Intelligence

, Volume 36, Issue 3, pp 664–684 | Cite as

DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique

  • Chumphol BunkhumpornpatEmail author
  • Krung Sinapiromsaran
  • Chidchanok Lursinsap


A dataset exhibits the class imbalance problem when a target class has a very small number of instances relative to other classes. A trivial classifier typically fails to detect a minority class due to its extremely low incidence rate. In this paper, a new over-sampling technique called DBSMOTE is proposed. Our technique relies on a density-based notion of clusters and is designed to over-sample an arbitrarily shaped cluster discovered by DBSCAN. DBSMOTE generates synthetic instances along a shortest path from each positive instance to a pseudo-centroid of a minority-class cluster. Consequently, these synthetic instances are dense near this centroid and are sparse far from this centroid. Our experimental results show that DBSMOTE improves precision, F-value, and AUC more effectively than SMOTE, Borderline-SMOTE, and Safe-Level-SMOTE for imbalanced datasets.


Classification Class imbalance Over-sampling Density-based 


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  1. 1.
    Bai X, Yang X, Yu D, Latecki LJ (2008) Skeleton-based shape classification using path similarity. Int J Pattern Recognit Artif Intell 22(4):733–746 CrossRefGoogle Scholar
  2. 2.
    Batista GEAPA, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor 6(1):20–29 CrossRefGoogle Scholar
  3. 3.
    Blake CL, Merz CJ (2009) UCI Repository of machine learning databases. Department of Information and Computer Sciences, University of California, Irvine, California, USA
  4. 4.
    Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(6):1145–1159 CrossRefGoogle Scholar
  5. 5.
    Buckland M, Gey F (1994) The relationship between recall and precision. J Am Soc Inf Sci 45(1):12–19 CrossRefGoogle Scholar
  6. 6.
    Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Theeramunkong T, Kijsirikul B, Cercone N, Ho T-B (eds) 13th Pacific-Asia conference on knowledge discovery and data mining, Bangkok, Thailand. Lecture notes in artificial intelligence, vol 5476. Springer, Heidelberg, pp 475–482 CrossRefGoogle Scholar
  7. 7.
    Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:341–378 Google Scholar
  8. 8.
    Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTEBoost: improving prediction of the minority class in boosting. In: The 7th European conference on principles and practice of knowledge discovery in databases, Cavtat-Dubrovnik, Croatia, pp 107–119 Google Scholar
  9. 9.
    Chawla NV, Japkowicz N, Kolcz A (2004) SIGKDD Explor 6(1):1–6. Editorial: Special Issue on Learning from imbalanced data sets CrossRefGoogle Scholar
  10. 10.
    Chiang I-J, Shieh M-J, Hsu JY, Wong J-M (2005) Building a medical decision support system for colon polyp screening by using fuzzy classification trees. Appl Intell 22(1):61–75. Special Issue: Foundations and Advances in Data Mining CrossRefGoogle Scholar
  11. 11.
    Cohen WW (1995) Fast effective rule induction. In: 12th international conference on machine learning, Lake Tahoe, California, USA, pp 115–123 Google Scholar
  12. 12.
    Corman TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms, 2nd edn. MIT Press, Cambridge Google Scholar
  13. 13.
    Cover T, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27 zbMATHCrossRefGoogle Scholar
  14. 14.
    Domingos P (1999) Metacost: a general method for making classifiers cost-sensitive. In: The 5th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, California, USA, pp 155–164 CrossRefGoogle Scholar
  15. 15.
    Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: The 2nd international conference on knowledge discovery and data mining, Portland, Oregon, USA, pp 226–231 Google Scholar
  16. 16.
    Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang D-S, Zhang X-P, Huang G-B (eds) The 2005 international conference on intelligent computing, Hefei, China. Lecture notes in computer science, vol 3644. Springer, Heidelberg, pp 878–887 Google Scholar
  17. 17.
    Hu X (2005) A data mining approach for retailing bank customer attrition analysis. Appl Intell 22(1):47–60. Special Issue: Foundations and Advances in Data Mining CrossRefGoogle Scholar
  18. 18.
    Japkowicz N (2000) The class imbalance problem: significance and strategies. In: 2000 international conference on artificial intelligence, Las Vegas, Nevada, USA, pp 111–117 Google Scholar
  19. 19.
    Japkowicz N (2003) Class imbalance: are we focusing on the right issue? In: 20th international conference on machine learning, Washington, District of Columbia, USA, pp 17–23 Google Scholar
  20. 20.
    Jungnickel D (2003) Graphs, networks and algorithms. Springer, Heidelberg Google Scholar
  21. 21.
    Kamber M, Han J (2000) Data mining: concepts and techniques, 2nd edn. Morgan Kaufman, San Mateo Google Scholar
  22. 22.
    Khor K-C, Ting C-Y, Phon-Amnuaisuk S (2010) A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection. Appl Intell. doi: 10.1007/s10489-010-0263-y Google Scholar
  23. 23.
    Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: 14th international conference on machine learning, Nashville, Tennessee, USA, pp 179–186 Google Scholar
  24. 24.
    Kubat M, Holte R, Matwin S (1997) Learning when negative examples abound. In: 9th European conference on machine learning, Prague, Czech Republic, pp 146–153 Google Scholar
  25. 25.
    Lewis DD, Catlett J (1994) Heterogeneous uncertainty sampling for supervised learning. In: 11th international conference on machine learning, New Brunswick, New Jersey, USA, pp 148–156 Google Scholar
  26. 26.
    Lu Y, Chen TQ, Hamilton B (1998) A fuzzy diagnostic model and its application in automotive engineering diagnosis. Appl Intell 9(3):231–243 CrossRefGoogle Scholar
  27. 27.
    Murphey YL, Chen ZH, Feldkamp LA (2008) An incremental neural learning framework and its application to vehicle diagnostics. Appl Intell 28(1):29–49 CrossRefGoogle Scholar
  28. 28.
    Prati RC, Batista GEAPA, Monard MC (2004) Class imbalances versus class overlapping: an analysis of a learning system behavior. In: Monroy R, Arroyo G, Sucar LE, Sossa H (eds) 3rd Mexican international conference on artificial intelligence, Mexico City, Mexico. Lecture notes in artificial intelligence, vol 2972, pp 312–321 Google Scholar
  29. 29.
    Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo Google Scholar
  30. 30.
    Tetko IV, Livingstone DJ, Luik AI (1995) Neural network studies. 1. Comparison of overfitting and overtraining. J Chem Inf Comput Sci 35(5):826–833 CrossRefGoogle Scholar
  31. 31.
    Tomek I (1976) Two modifications of CNN. IEEE Trans Syst Man Cybern 6(11):769–772 MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Chumphol Bunkhumpornpat
    • 1
    Email author
  • Krung Sinapiromsaran
    • 1
  • Chidchanok Lursinsap
    • 1
  1. 1.Department of Mathematics, Faculty of ScienceChulalongkorn UniversityBangkokThailand

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