DBSMOTE: Density-based synthetic minority over-sampling technique

Applied Intelligence

Volumn 36, Issue 3, 2012, Pages 664-684

  Bunkhumpornpat, Chumphol ^a   Sinapiromsaran, Krung ^a   Lursinsap, Chidchanok ^a  

^a CHULALONGKORN UNIVERSITY   (Thailand)

Author keywords

Class imbalance; Classification; Density based; Over sampling

Indexed keywords

CLASS IMBALANCE; CLASS IMBALANCE PROBLEMS; DATA SETS; DENSITY-BASED; IMBALANCED DATA-SETS; INCIDENCE RATE; OVER SAMPLING; SHORTEST PATH; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES; TARGET CLASS;

CLASSIFICATION (OF INFORMATION);

ARTIFICIAL INTELLIGENCE;

EID: 84862140885     PISSN: 0924669X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10489-011-0287-y     Document Type: Article

References (31)

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
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
3. Blake CL, Merz CJ (2009) UCI Repository of machine learning databases. http://archive.ics.uci.edu/ml/. Department of Information and Computer Sciences, University of California, Irvine, California, USA
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 (Pubitemid 127406521)
5. Buckland M, Gey F (1994) The relationship between recall and precision. J Am Soc Inf Sci 45(1):12-19
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: Theer-amunkong 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
7. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:341-378
8. Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) SMOTE-Boost: 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 (Pubitemid 37231089)
9. Chawla NV, Japkowicz N, Kolcz A (2004) SIGKDD Explor 6(1):1-6. Editorial: Special Issue on Learning from imbalanced data sets
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
11. Cohen WW (1995) Fast effective rule induction. In: 12th international conference on machine learning, Lake Tahoe, California, USA, pp 115-123
12. Corman TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms, 2nd edn. MIT Press, Cambridge
13. Cover T, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21-27
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
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
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 (Pubitemid 41491129)
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
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
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
20. Jungnickel D (2003) Graphs, networks and algorithms. Springer, Heidelberg
21. Kamber M, Han J (2000) Data mining: concepts and techniques, 2nd edn. Morgan Kaufman, San Mateo
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
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
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 (Pubitemid 127061922)
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
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 (Pubitemid 128512703)
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
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 (Pubitemid 38716795)
29. Quinlan JR (1992) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo
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
31. Tomek I (1976) Two modifications of CNN. IEEE Trans Syst Man Cybern 6(11):769-772