Predicting minority class for suspended particulate matters level by extreme learning machine

Neurocomputing

Volumn 128, Issue , 2014, Pages 136-144

  Vong, Chi Man ^a   Ip, Weng Fai ^a   Wong, Pak Kin ^a   Chiu, Chi Chong ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Extreme learning machine (ELM); Imbalance problem; Prior duplication; Support vector machine (SVM)

Indexed keywords

FORECASTING; KNOWLEDGE ACQUISITION; SUPPORT VECTOR MACHINES;

EXPERIMENTAL COMPARISON; EXTREME LEARNING MACHINE; IMBALANCE PROBLEM; NUMERICAL VALUES; PREDICTION MODEL; PRIOR DUPLICATION; SUSPENDED PARTICULATE MATTERS; TRAINING DATASET;

PARTICLES (PARTICULATE MATTER);

ACCURACY; ARTICLE; CONTROLLED STUDY; DATA ANALYSIS; ENVIRONMENTAL MONITORING; EXTREME LEARNING MACHINE; INTERMETHOD COMPARISON; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; PREDICTION; PRIORITY JOURNAL; PROBABILITY; STRATEGIC PLANNING; SUPPORT VECTOR MACHINE; SUSPENDED PARTICULATE MATTER;

EID: 84893691788     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.11.056     Document Type: Article

References (36)

1. 10 hourly concentrations, in the Greater Area of Athens, Greece. Atmos. Environ. 2006, 40:1216-1229.
2. 2 concentrations in highly polluted industrial areas of complex terrain. Atmos. Environ. Part B. Urban Atmos. 1993, 27:221-230.
3. Comrie A.C. Comparing neural networks and regression models for ozone forecasting. J. Air. Waste Manage. Assoc. 1997, 47:653-663.
4. Gardner M.W., Dorling S.R. Artificial neural networks (the multilayer perceptron)-A review of applications in the atmospheric sciences. Atmos. Environ. 1998, 32:2627-2636.
5. 2 concentrations in urban air in London. Atmos. Environ. 1999, 33:709-719.
6. 10 concentrations 30h in advance in Santiago, Chile. Atmos. Environ. 2002, 36:4555-4561.
7. Lu W.Z., Wang W.J. Potential assessment of the support vector machine method in forecasting ambient air pollutant trends. Chemosphere 2005, 59:693-701.
8. Osowski S., Garanty K. Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng. Appl. Artif. Intell. 2007, 20:745-755.
9. Lu W.-Z., Wang D. Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme. Sci. Total Environ. 2008, 395:109-116.
10. 2 concentrations by support vector machines and neural networks using principal component analysis. Simulation Modelling Pract. Theory 2008, 16:1488-1502.
11. J.Y. Yang, W.F. Ip, C.M. Vong, and P.K. Wong, Effect of choice of kernel in support vector machines on ambient air pollution forecasting,in: Proceedings of the 2011 International Conference on System Science and Engineering, ICSSE, 2011, pp. 552-557.
12. Murphey Y.L., Guo H., Feldkamp L.A. Neural learning from unbalanced data. Appl. Intell. 2004, 21:117-128.
13. Huang G.-B., Zhu Q.-Y., Siew C.-K. Extreme learning machine: Theory and applications. Neurocomputing 2006, 70:489-501.
14. Sun Z.-L., Au K.-F., Choi T.-M. A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 2007, 37:1321-1331.
15. Zhang R., Huang G.-B., Sundararajan N., Saratchandran P. Multicategory classification using an extreme learning machine for microarray gene expression cancer diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinf. 2007, 4:485-495.
16. Nizar A.H., Dong Z.Y., Wang Y. Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans. Power Syst. 2008, 23:946-955.
17. Suresh S., Saraswathi S., Sundararajan N. Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng. Appl. Artif. Intell. 2010, 23:1149-1157.
18. Xu Y., Dong Z.Y., Meng K., Zhang R., Wong K.P. Real-time transient stability assessment model using extreme learning machine. Gener., Transm. Distrib., IET 2011, 5:314-322.
19. Savitha R., Suresh S., Sundararajan N. Fast learning circular complex-valued extreme learning machine (CC-ELM) for real-valued classification problems. Inf. Sci. 2012, 187:277-290.
20. Huang G.-B., Zhou H., Ding X., Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 2012, 42:513-529.
21. Blagus R., Lusa L. Class prediction for high-dimensional class-imbalanced data. BMC Bioinf. 2010, 11:523.
22. Batista G.E.A.P.A., Prati R.C., Monard M.C. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl. 2004, 6:20-29.
23. Estabrooks A., Jo T., Japkowicz N. A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 2004, 20:18-36.
24. Ducange P., Lazzerini B., Marcelloni F. Multi-objective genetic fuzzy classifiers for imbalanced and cost-sensitive datasets. Soft Comput. - A Fusion Found., Methodol. Appl. 2010, 14:713-728.
25. Wu G., Chang E.Y. KBA: kernel boundary alignment considering imbalanced data distribution. IEEE Trans. Knowl. Data Eng. 2005, 17:786-795.
26. Tang Y., Zhang Y.-Q., Chawla N.V., Krasser S. SVMs modeling for highly imbalanced classification. IEEE Trans. Syst., Man, Cybern., Part B: Cybern. 2009, 39:281-288.
27. Y. Sun, M.S. Kamel, and Y. Wang, Boosting for learning multiple classes with imbalanced class distribution, in: Proceedings of the Sixth International Conference on Data Mining, ICDM'06, 2006, pp. 592-602.
28. Ou G., Murphey Y.L. Multi-class pattern classification using neural networks. Pattern Recognition 2007, 40:4-18.
29. Bartlett P.L. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inf. Theory 1998, 44:525-536.
30. SMG. E-publication Download Page, available from: http://www.smg.gov.mo/www/ccaa/pdf/e_pdf_download.php (Last accessed: 09.05.12).
31. G.-B. Huang, MATLAB Codes of ELM Algorithm, available from: (Last accessed: 22.03.12). http://www.ntu.edu.sg/home/egbhuang/ELM_Codes.htm.
32. Stigler S.M. Francis Galton's account of the invention of correlation. Stat. Sci. 1989, 4:73-79.
33. SMG, Air Quality Index Bulletin, available from: (Last accessed: 09.05.12). http://www.smg.gov.mo/www/ccaa/iqa/fe_iqa.htm.
34. C.-C. Chang, C.-J. Lin, LIBSVM: A Library For Support Vector Machines, available from: (Last access: 21.03.12). http://www.csie.ntu.edu.tw/~cjlin/libsvm.
35. H.-T. Lin, C.-J. Lin, "A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods," 2003.
36. Hsu C.-W., Lin C.-J. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 2002, 13:415-425.