Handling class imbalance problem in cultural modeling

2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009

Volumn , Issue , 2009, Pages 251-256

  Peng, Su ^a,c   Wenji, Mao ^a   Zeng, Daniel ^a,b   Xiaochen, Li ^a   Wang, Fei Yue ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b UNIVERSITY OF ARIZONA   (United States)

^c SHANDONG JIANZHU UNIVERSITY   (China)

Author keywords

Class imbalance problem; Classification; Cultural modeling; ROC analysis; Sampling

Indexed keywords

BEHAVIORAL MODEL; CLASS IMBALANCE PROBLEM; CLASS IMBALANCE PROBLEMS; CLASSIFICATION; CLASSIFICATION ALGORITHM; CULTURAL MODELING; GROUP BEHAVIOR; MACHINE LEARNING METHODS; RECEIVER OPERATING CHARACTERISTIC ANALYSIS; RESEARCH AREAS; ROC ANALYSIS; SOCIAL COMPUTING;

CLASSIFIERS; INTERSYMBOL INTERFERENCE;

LEARNING SYSTEMS;

EID: 70350054926     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISI.2009.5137320     Document Type: Conference Paper

References (29)

1. Subrahmanian V S, "Computer science: cultural modeling in real time," Science, 2007, 317(5844): 1509-1510.
2. Subrahmanian V S, Albanese M, Martinez M V, Nau D, Reforgiato D, Simari G I, Sliva A, Wilkenfeld J, Udrea O, "CARA: a cultural-reasoning architecture," IEEE Intelligent Systems, 2007, 22(2): 12-16.
3. Khuller S, Martinez V, Nau D, Simari G, Sliva A, Subrahmanian V S, "Finding most probable worlds of logic programs," In Proc. the First International Conference on Scalable Uncertainty Management, Washington DC, USA, October 10-12, 2007, pp. 45-59.
4. Martinez V, Simari G I, Sliva A, Subrahmanian V S, "CONVEX: context vectors as a paradigm for learning group behaviors based on similarity," IEEE Intelligent Systems, 2007, 23(4): 51-57.
5. Wang F-Y, "Is culture computable?" IEEE Intelligent Systems, 2009, 24(2): 2-3.
6. Wang F-Y, Carley K M, Zeng D, Mao W, "Social computing: from social informatics to social intelligence," IEEE Intelligent Systems, 2007, 22(2): 79-83.
7. Wang F-Y, "Toward a paradigm shift in social computing: the ACP approach," IEEE Intelligent Systems, 2007, 22(5):65-67.
8. Zeng D, Wang F-Y, Carley K M, "Social computing," IEEE Intelligent Systems, 2007, 22(5): 20-22.
9. Minorities at Risk Project. College Park, MD: Center for International Development and Conflict Management. 2005 Retrieved from http://www.cidcm.umd. edu/mar/.
10. Kubat, M., Holte, R. and Matwin, S, "Machine learning for the detection of oil spills in satellite radar images," Machine Learning, 1998, 30:195-215.
11. Fawcett, T. E. and Provost, F, "Adaptive fraud detection," Data Mining and Knowledge Discovery, 1997, 1(3):291-316.
12. Japkowicz, N. Myers, C. and Gluck, M, "A novelty detection approach to classification," Proceedings of the Fourteenth Joint Conference on Artificial Intelligence, 2005, pp.518-523.
13. Li X, Mao W, Zeng D, Su P, Wang F-Y, "Performance evaluation of classification methods in cultural modeling," submitted to the Second International Workshop on Social Computing, 2009.
14. Chawla, N.V., Japkowicz, N. and Kolcz, A, "Editorial: special issue on learning from imbalanced data sets," SIGKDD Explorations, 2004, 6(1):1-6.
15. Chawla, N., Bowyer, K., Hall, L. and Kegelmeyer, W. P, "SMOTE: synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, 2002, 16:321-357.
16. Estabrooks, A, "A combination scheme for inductive learning from imbalanced data sets," Master Thesis, Faculty of Computer Science, Dalhousie University. Halifax, Nova Scotia, Canada, 2000.
17. Japkowicz, N, "The Class imbalance problem: significance and strategy," Proceedings of the 2002 International Conference on Artificial Intelligence: Special Track on Inductive Learning, 2000, vol.1, pp.111-117, LasVegas, NV.
18. Drummond, C. and Holte, R.C, "C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling," In: Working Notes of the ICML 2003 Workshop on Learning from Imbalanced Data sets, 2003, Washington, DC.
19. Estabrooks A., Jo, T., and Japkowicz, N., (2004), A Multiple Resampling Method for Learning from Imbalances Data Sets, in Computational Intelligence, Volume 20, Number 1, 2004.
20. Tan, P., Steinbach, M. and Kumar, V, "Introduction to data mining," Beijing: Posts & Telecom Press, 2006, pp.217-226.
21. Weiss, G, "Mining with Rarity: A unifying framework," SIGKDD Explorations, 2004, 6(1):7-19.
22. Ypma, A. and Duin, R, "Support objects for domain approximation," Proceedings of the Eighth International Conference on Artificial Neural Networks, 1998, pp.719-724, Springer.
23. Manevitz, L. M. and Yousef, M, "One-class SVMs for document classification," Journal of Machine Learning Research, 2001, 2:139-154.
24. Domingos, P, "MetaCost: a general method for making classifiers cost-sensitive," Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, 1999, pp. 155-164, ACM Press.
25. Zhou, Z.-H. and Liu X.-Y, "The influence of class imbalance on cost-sensitive learning: An Empirical Study," Proceedings of the Sixth IEEE International Conference on Data Mining, 2006, pp.970-974, IEEE Press.
26. Dudoit, S. and Fridlyand, J, "Classification in microarray experiments," In: T. P. Speed (Ed.), Statistical Analysis of Gene Expression Microarray Data. Boca Raton, FL: Chapman & Hall/CRC Press, 2003.
27. Forman, G, "An extensive empirical study of feature selection metrics for text classification," Journal of Machine Learning Research, Special Issue on Variable and Feature Selection, 2003, 3:1289-1305.
28. Zheng, Z., Wu, X. and Srihari, R, "Feature selection for text categorization on imbalanced data," SIGKDD Explorations, 2004, 6(1):80-89.
29. Japkowicz, N, "Concept-learning in the presence of between-class and within-class imbalances," Proceedings of the Fourteenth Conference of the Canadian Society for Computational Studies of Intelligence, 2001, pp. 67-77, Ottawa, Canada.