Skip to main content
SpringerLink
Log in

DBMUTE: density-based majority under-sampling technique

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Class imbalance is a challenging problem that demonstrates the unsatisfactory classification performance of a minority class. A trivial classifier is biased toward minority instances because of their tiny fraction. In this paper, our density function is defined as the distance along the shortest path between each majority instance and a minority-cluster pseudo-centroid in an underlying cluster graph. A short path implies highly overlapping dense minority instances. In contrast, a long path indicates a sparsity of instances. A new under-sampling algorithm is proposed to eliminate majority instances with low distances because these instances are insignificant and obscure the classification boundary in the overlapping region. The results show predictive improvements on a minority class from various classifiers on different UCI datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Blake CL, Merz CJ (1998) The UC Irvine machine learning repository. http://archive.ics.uci.edu/ml/. University of California, Irvine, CA

  2. 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

    Article  Google Scholar 

  3. Buckland M, Gey F (1994) The relationship between recall and precision. J Am Soc Inf Sci 45(1):12–19

    Article  Google Scholar 

  4. Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2012) DBSMOTE: density-based synthetic minority over-sampling technique. Appl. Intell. 36(3):664–684

    Article  Google Scholar 

  5. Bunkhumpornpat C, Sinapiromsaran, K, Lursinsap C (2011) MUTE: majority under-sampling technique. In: The 8th international conference on information, communications, and signal processing, Singapore

  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) The 13th Pacific-Asia conference on knowledge discovery and data Mining, Bangkok, Thailand, vol 5476., Lecture notes in artificial intelligence. Springer, Heidelberg, pp 475–482

  7. Chawla NV (2010) Data mining for imbalanced datasets: an overview. In: Maimon O, Rokach L (eds) Data mining and knowledge discovery handbook. Springer, Berlin, pp 875–886

    Google Scholar 

  8. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:341–378

    MATH  Google Scholar 

  9. 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. Special issue: foundations and advances in data mining. Appl Intell 22(1):61–75

    Article  Google Scholar 

  10. Cohen WW (1995) Fast effective rule induction. In: 12th international conference on machine learning. Lake Tahoe, CA, pp 115–123

  11. Cover T, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  MATH  Google Scholar 

  12. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  13. Drummond C, Holte RC (2003) C4.5, Class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: the ICML 2003 workshop on learning from imbalanced datasets II, Washington, DC, pp 1–8

  14. 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

  15. Frank E, Bouckaert RR (2006) Naive Bayes for text classification with unbalanced classes. In: The 10th European conference on principles and practice of knowledge discovery in databases, Berlin, Germany, pp 503–510

  16. Garcia V, Sánchez JS, Mollineda RA, Alejo R, Sotoca JM (2007) The class imbalance problem in pattern classification and learning. IV Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA 2007). Zaragoza, Spain, pp 283–291

  17. 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

  18. Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann, Burlington

    MATH  Google Scholar 

  19. Hu X (2005) A data mining approach for retailing bank customer attrition analysis. Special issue: foundations and advances in data mining. Appl Intell 22(1):47–60

    Article  Google Scholar 

  20. Japkowicz N (2000) The class imbalance problem: significance and strategies. In: The 2000 international conference on artificial intelligence, Las Vegas, NV, pp 111–117

  21. Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6(5):429–449

    MATH  Google Scholar 

  22. Khor K-C, Ting C-Y, Phon-Amnuaisuk S (2012) A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection. Appl Intell 36(2):320–329

    Article  Google Scholar 

  23. Kubat M, Holte R, Matwin S (1998) Machine learning for the detection of oil spills in satellite radar images. Mach Learn 30(2–3):195–215

    Article  Google Scholar 

  24. Murphey YL, Chen ZH, Feldkamp LA (2008) An incremental neural learning framework and its application to vehicle diagnostics. Appl Intell 28(1):29–49

    Article  Google Scholar 

  25. Murty MN, Devi VS (2012) Pattern recognition: an algorithmic approach. Springer, Berlin

    MATH  Google Scholar 

  26. Nguyen GH, Bouzerdoum A, Phung SL (2009) Learning Pattern Classification Tasks with Imbalanced Data Sets. In: Yin P (ed) Pattern recognition. In-Teh, Vukovar, pp 193–208

  27. 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.) The 3rd Mexican international conference on artificial intelligence, Mexico City, Mexico. Lecture Notes in artificial intelligence, vol 2972, pp 312–321

  28. Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, Burlington

    Google Scholar 

  29. 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

    Article  Google Scholar 

  30. Tomek I (1976) Two modifications of CNN. IEEE Trans Syst Man Cybern 6(11):769–772

    Article  MathSciNet  MATH  Google Scholar 

  31. Wang S, Li Z, Chao W-H, Cao Q (2012) Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning. In: IJCNN, pp 1–8

  32. Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, Burlington

    Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge that this research is fully funded by a research Grant for new scholars from the Thailand Research Fund (TRG5680082), Year 2013. In addition, we would like to thank the Research Administration Center, Chiang Mai University, for the proofreading service.

Author information

Authors and Affiliations

  1. Theoretical and Empirical Research Group, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand

    Chumphol Bunkhumpornpat

  2. Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand

    Krung Sinapiromsaran

Authors
  1. Chumphol Bunkhumpornpat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krung Sinapiromsaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chumphol Bunkhumpornpat.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bunkhumpornpat, C., Sinapiromsaran, K. DBMUTE: density-based majority under-sampling technique. Knowl Inf Syst 50, 827–850 (2017). https://doi.org/10.1007/s10115-016-0957-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-016-0957-5

Keywords

Search

Navigation