RWO-Sampling: A random walk over-sampling approach to imbalanced data classification

Information Fusion

Volumn 20, Issue 1, 2014, Pages 99-116

  Zhang, Huaxiang ^a   Li, Mingfang ^b  

^a SHANDONG NORMAL UNIVERSITY   (China)

^b Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology   (China)

Author keywords

Data fusion; Imbalanced data classification; Over sampling; Probability distribution; Random walk

Indexed keywords

DATA FUSION; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES;

CLASS IMBALANCE PROBLEMS; CLASSIFICATION PERFORMANCE; EXPERIMENTAL EVALUATION; IMBALANCED DATA; IMBALANCED DATA SETS; OVER SAMPLING; RANDOM WALK; STANDARD DEVIATION;

CLASSIFICATION (OF INFORMATION);

EID: 84901596053     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2013.12.003     Document Type: Article

References (28)

1. R. Barandela, J.S. Sanchez, V. Garcia, and E. Rangel Strategies for learning in class imbalance problems Pattern Recogn. 36 2003 849 851
2. Z.-H. Zhou, and X.-Y. Liu Training cost-sensitive neural networks with methods addressing the class imbalance problem IEEE Trans. Knowl. Data Eng. 18 2006 63 77 (Pubitemid 43145089)
3. G.E.A.P.A. Batista, R.C. Prati, and M.C. Monard A study of the behavior of several methods for balancing machine learning training data ACM SIGKDD Explor. Newsl. 6 1 2004 20 29
4. A. Sun, E.-P. Lim, and Y. Liu On strategies for imbalanced text classification using SVM: a comparative study Decis. Support Syst. 48 1 2009 191 201
5. S. Kotsiantis, D. Kanellopoulos, and P. Pintelas Handling imbalanced datasets: a review GESTS Int. Trans. Comput. Sci. Eng. 30 2006 1 12
6. S.-J. Yen, and Y.-S. Lee Cluster-based under-sampling approaches for imbalanced data distributions Expert Syst. Appl. 36 2009 5718 5727
7. T.M. Mitchell Machine Learning 1997 McGraw Hill
8. N. Chawla, K. Bowyer, L. Hall, and W.P. Kegelmeyer SMOTE: synthetic minority over-sampling technique J. Artif. Intell. Res. 16 2002 321 357 (Pubitemid 43057176)
9. I. Tomek Two modifications of CNN IEEE Trans. Syst. Man Commun. SMC-6 1976 769 772
10. H. Zhang, and Z. Wang A normal distribution-based over-sampling approach to imbalanced data classification Lect. Notes Artif. Intell. 7120 2011 83 96
11. H. Han, W.-Y. Wang, and B.-H. Mao Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning Lect. Notes Comput. Sci. 3644 2005 878 887 (Pubitemid 41491129)
12. H. Guo, and H.L. Viktor Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach SIGKDD Explor. Newsl. 6 2004 30 39
13. A. Estabrooks, T. Jo, and N. Japkowicz A multiple resampling method for learning from imbalanced data sets Comput. Intell. 20 1 2004 18 36
14. Y. Peng, J. Yao, AdaOUBoost: adaptive over-sampling and under-sampling to boost the concept learning in large scale imbalanced data sets, in: MIR'10, Philadelphia, Pennsylvania, USA, March, 2010, pp. 111-118.
15. X. Zhu, Lazy bagging for classifying imbalanced data, in: 17th IEEE International Conference on Data Mining, 2007, pp. 763-768.
16. Y. Sun, M.S. Kamel, A.K.C. Wong, and Y. Wang Cost-sensitive boosting for classification of imbalanced data Pattern Recogn. 40 2007 3358 3378 (Pubitemid 47223287)
17. M. Woźniak, M. Grana, and E. Corchado A survey of multiple classifier systems as hybrid systems Inf. Fusion 16 2014 3 17
18. X. Li, L. Wang, and E. Sung AdaBoost with SVM-based component classifiers Eng. Appl. Artif. Intell. 21 2008 785 795
19. G. Giacinto, R. Perdisci, M.D. Rio, and F. Roli Intrusion detection in computer networks by a modular ensemble of one-class classifiers Inf. Fusion 9 2008 69 82 (Pubitemid 47589059)
20. C. Drummond, R.C. Holte, C4.5, Class imbalance and cost sensitivity: why under-sampling beats over-sampling, in: Proceedings of the ICML'03 Workshop on Learning from Imbalanced Data Sets, 2003.
21. Z. Zheng, X. Wu, and R. Srihari Feature selection for text categorization on imbalanced data SIGKDD Explor. Newsl. 6 1 2004 80 89
22. M.-C. Chen, L.-S. Chen, C.-C. Hsu, and W.-R. Zeng An information granulation based data mining approach for classifying imbalanced data Inf. Sci. 178 2008 3214 3227
23. Y. Tang, Y. Zhang, N. Chawla, and S. Krasser SVMs modeling for highly imbalanced classification IEEE Trans. Syst. Man Cybern. Part B 39 1 2009 281 288
24. M.A. Mazurowski, P.A. Habas, J.M. Zurada, J.Y. Lob, and J.A. Baker Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance Neural Networks 21 2008 427 436
25. A. Fernández, M.J.d. Jesus, F. Herrera. Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets, doi: 10.1016/j.ijar.2008.11.004.
26. F. Provost, T. Fawcett, and R. Kohavi The case against accuracy estimation for comparing induction algorithms Proceedings of the Fifteenth International Conference on Machine Learning 1998 Morgan Kaufmann San Francisco, CA 445 453
27. G. Wu, and E.Y. Chang KBA: kernel boundary alignment considering imbalanced data distribution IEEE Trans. Knowl. Data Eng. 17 6 2005 786 795 (Pubitemid 40860458)
28. M. Kubat, and S. Matwin Addressing the curse of imbalanced training sets: one-sided selection Proceedings of the Fourteenth International Conference on Machine Learning 1997 Morgan Kaufmann San Francisco, CA 179 186