A hybrid algorithm applied to classify unbalanced data

Proceeding - 6th International Conference on Networked Computing and Advanced Information Management, NCM 2010

Volumn , Issue , 2010, Pages 618-621

  Lee, C Y ^a   Yang, M R ^b   Chang, L Y ^b   Lee, Z J ^b  

^a LAN YANG INSTITUTE OF TECHNOLOGY   (Taiwan)

^b HUAFAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; DATA SETS; EXISTING METHOD; FEATURE SELECTION; FEATURE SUBSET; HYBRID ALGORITHMS; OVER SAMPLING; SIMULATION RESULT; UNBALANCED DATA;

DECISION TREES; FEATURE EXTRACTION; INFORMATION MANAGEMENT; PARTICLE SWARM OPTIMIZATION (PSO); STATISTICAL TESTS;

ALGORITHMS;

EID: 77958600087     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. Blake C, Keogh E, Merz CJ (1998) UCI repository of machine learning databases [http://www.ics.uci.edu/»mlearn/MLRepository.html), Department of Information and Computer Science, University of California, Irvine, CA.
2. Chawla NV, Japkowicz N, Kotcz A (2003) Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Data Sets.
3. Friedman JH (1997) On bias, variance, 0/1-loss and the curse of dimensionality, Data Mining and Knowledge Discovery 1:55-77.
4. Fu X, Wang L, Chua KS, Chu F (2002) Training RBF neural networks on unbalanced data, Proceedings of the 9th International Conference on Neural Information Processing 2:1016-1020.
5. Ghoshal SP, Chatterjee A, Mukherjee V (2009) Bio-inspired fuzzy logic based tuning of power system stabilizer, Expert Systems with Applications 36:9281-9292.
6. Güvenir HA, Sirin Ï (1996) Classification by feature partitioning, Machine Learning 23:47-67.
7. Güvenir HA, Ko HG (1998) Concept representation with overlapping feature intervals, Cybernetics and Systems: An International Journal 29:263-282.
8. Hazra J, Sinha AK (2008) Environmental constrained economic dispatch using bacteria foraging optimization, Joint International Conference on Power System Technology and IEEE Power India Conference, pp 1-6.
9. Hogg RV, Ledolter J (1987) Engineer statistics. England: MacMillan Publishing Company.
10. Japkowicz N (2000) Proceedings of the AAAI'2000 Workshop on Learning from Imbalanced Data Sets.
11. Jiang Y, Zhou ZH (2004) Training data for kNN classifiers with neural network ensemble. ISNN 1:1-6.
12. Kim DS, Nguyen HN, Park JS (2005) Genetic algorithm to improve SVM based network intrusion detection system, 19th International Conference on Advanced Information Networking and Applications 2: 155-158. (Pubitemid 43804002)
13. Lee U, Magistretti E, Gerla M, Bellavista P, Lió P, Lee KW (2009) Bio-inspired multi-agent data harvesting in a proactive urban monitoring environment, Ad Hoc Networks 7:725-74l.
14. Lee ZJ (2008) An integrated algorithm for gene selection and classification applied to microarray data of ovarian cancer, International Journal Artificial Intelligence in Medicine 42:81-93.
15. Liao Y, Fang SC, Nuttle HLW(2004) A neural network model with bounded-weights for pattern classification, Computers and Operations Research 31:1411-1426. (Pubitemid 38427641)
16. Lin SW, Ying KC, Chen SC, Lee ZJ (2008) Particle swarm optimization for parameter determination and feature selection of support vector machines, Expert Systems With Applications 35:1817-1824.
17. Lin SW, Lee ZJ, Chen SC, Tseng TY (2008) Parameter determination of support vector machines and feature selection using simulated annealing approach, Applied Soft Computing 8: 1505-1512.
18. Liu H, Motoda H (1998) Feature selection for knowledge discovery and data mining, Kluwer Academic.
19. Lu SJ (2006) Gene expression analysis and regulator pathway exploration with the use of microarray data for ovarian cancer, Master thesis of National Taiwan University of Science and Technology.
20. Lu Y, Guo H, Feldkamp L (1998) Robust neural learning from unbalanced data samples, The 1998 IEEE International Joint Conference on Neural Networks 3:1816-182l.
21. Mladenic D, Grobelnik M (2003) Feature selection on hierarchy of web documents, Decision Support Systems 35:45-87.
22. Padmaja TM, Dhulipalla N, Bapi RS, Krishna PR (2007) Unbalanced data classification using extreme outlier elimination and sampling techniques for fraud detection, International Conference on Advanced Computing and Communications, pp 511-516.
23. Panigrahi BK, Pandi VR (2009) Congestion management using adaptive bacterial foraging algorithm, Energy Conversion and Management 50: 1202-1209.
24. Passino KM (2000) Distributed optimization and control using only a germ of intelligence, Proceedings of the 2000 IEEE International Symposium on Intelligent Control.
25. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control, IEEE Control Systems Magazine 22:52-67. (Pubitemid 34711477)
26. Polat K, Günes S (2006) Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS), Biomedical Signal Processing and Control 1:253-260.
27. Searle, Shayle R (1987) Linear models for unbalanced data, Wiley.
28. Tao Q, Wu GW, Wang FY, Wang J (2005) Posterior probability support vector Machines for unbalanced data, IEEE Transactions on Neural Networks 16:1561-1573. (Pubitemid 41709652)
29. Tang Y, Zhang YQ, Chawla NV, Krasser S (2009) SVMs modeling for highly imbalanced classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B 39:281-288.
30. Tripathy M, Mishra S (2007) Bacteria foraging-based solution to optimize both real power loss and voltage stability limit, IEEE Transactions on Power Systems 22:240-248. (Pubitemid 46437733)
31. Wang J, Jean J (1993) Resolve Multifont character confusion with neural network, Pattern Recognition 26: 173-187.
32. Wang J, Miyazaki M, Kameda H, Li J (2000) Improving performance of parallel transaction processing systems by balancing data load on line, Seventh International Conference on Parallel and Distributed Systems, pp 331-338..
33. Weiss S, Indurkhya N (1995) Rule-based machine learning methods for functional prediction, Journal of Artificial Intelligence Research 3:383-403.
34. Yang X, Song Q, Cao A (2004) Clustering nonlinearly separable and unbalanced data set, 2004 2nd International IEEE Conference on Intelligent Systems 2:491-496.
35. Ye D, Chen Z (2008) A rough set based minority class oriented learning algorithm for highly unbalanced data sets, IEEE International Conference on Granular Computing, pp 736-739.
36. Zhang Y, Wu L (2009) Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network, Expert Systems with Applications 36: 8849-8854.
37. Zhang J, Bloedorn E, Rosen L, Venese D (2004) Learning rules from highly unbalanced data sets, Fourth IEEE International Conference on Data Mining, ICDM '04. 1-4:571-574.
38. Zhang J, Mani I (2003) KNN approach to unbalanced data distributions: A case study involving information extraction, Proceedings of the ICML'2003 Workshop on Learning from Imbalanced Datasets.