A one-class classification framework using SVDD: Application to an imbalanced geological dataset

IEEE TechSym 2014 - 2014 IEEE Students' Technology Symposium

Volumn , Issue , 2014, Pages 76-81

  Chaki, Soumi ^a   Verma, Akhilesh Kumar ^a   Routray, Aurobinda ^a   Mohanty, William K ^a   Jenamani, Mamata ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

g metric mean; imbalanced dataset; one class classification; support vector data description

Indexed keywords

DATA DESCRIPTION; GAMMA RAYS; NEUTRON LOGGING;

CLASSIFICATION ANALYSIS; G-METRIC MEAN; IMBALANCED DATASET; ONE-CLASS CLASSIFICATION; PERFORMANCE EVALUATOR; PETROPHYSICAL PROPERTIES; SUPERVISED CLASSIFICATION; SUPPORT VECTOR DATA DESCRIPTION;

CLASSIFICATION (OF INFORMATION);

EID: 84901807556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/TechSym.2014.6807918     Document Type: Conference Paper

References (25)

1. M. Nikravesh, "Past, present and future intelligent reservoir characterization trends," Journal of petroleum science and engineering, vol. 31, pp. 67-79, 2001.
2. A. Ouenes, "Practical application of fuzzy logic and neural networks to fractured reservoir characterization," Computers and Geosciences, vol. 26, pp. 953-962, 2000.
3. S. Chaki, A. K. Verma, A. Routray, M. Jenamani, W. K. Mohanty, P. K. Chaudhuri, S. K. Das, " Prediction of Porosity and Sand Fraction from Well Log Data using ANN and ANFIS: a comparative study," 10th Biennial International Conference & Exposition of SPG, India, 2013, in press.
4. A. K. Verma, S. Chaki, A. Routray, M. Jenamani, W. K. Mohanty, P. K. Chaudhuri, S. K. Das, "Quantifying Sand Fraction from Seismic Attributes using Modular Artificial Neural Network," 10th Biennial International Conference & Exposition of SPG, India, 2013, in press.
5. S. Mohaghegh, R. Arefi, S. Ameri, and M. H. Hefner, "A methodological approach for reservoir heterogeneity characterization using artificial neural networks," in the SPE Annual Technical Conference and Exhibition, SPE 28394, pp. 1-5, New Orleans, LA, U. S. A., 25-28 September, 1994.
6. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Trans. Knowledge and Data Eng., vol. 21, no. 9, pp. 1263-1284, Sept., 2009.
7. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
8. M. Chi and L. Bruzzone, "A semilabeled-sample-driven bagging technique for ill-posed classification problems," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 1, pp. 69-73, Jan. 2005.
9. L. Bruzzone, M. Chi, and M. Marconcini, "Transductive SVMs for semisupervised classification in ill-posed problems," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, Nov. 2006.
10. G. Mercier and F. Girard-Ardhuin, "Partially supervised oil-slick detection by SAR imagery using kernel expansion," IEEE Trans. Geosci. Remote Sens., vol. 4, no. 10, pp. 2839-2846, Oct. 2006.
11. D. Tax, "One-class classification, Concept-learning in the absence of counter-examples," Ph. D. dissertation, Delft Univ. Technol., Delft, The Netherlands, 2001.
12. J. Munoz-Mari, L. Bruzzone, and G. Camps-Valls, "A Support Vector Domain Description approach to supervised classification of remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 8, pp. 2683-2692, Aug. 2007.
13. D. Tax, R. Duin, "Support vector domain description," Pattern Recognition Letters, vol. 20, no. 11-13, pp. 1191-1199, 1999.
14. D. Tax, R. Duin, "Support vector data description," Machine Learning vol. 54, no. 1, pp. 45-66, 2004.
15. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge, U. K.: Cambridge Univ. Press, 2004.
16. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern clasiification, Wiley, New York, 2001.
17. J. M. Busch, W. G. Fortney, and L. N. Berry, "Determination of lithology from well logs by statistical analysis," SPE Form Evaluation, vol. 2, pp. 412-418, 1987.
18. G. J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition, Wiley, New York, 1992.
19. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," In: Haussler, D. (Ed.), Proceedings of the Annual Conference on Computational Learning Theory. ACM Press, Pittsburgh, PA, pp. 144-152, 1992.
20. V. Vapnik, S. Golowich, and A. Smola "Support vector method for function approximation, regression estimation, and signal processing," In: Mozer, M. C., Jordan, M. I., Petsche, T. (Eds.), Advances in Neural Information Processing Systems, vol. 9. MA, MIT Press, Cambridge, pp. 281-287, 1997.
21. A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. N. Vapnik, "Support Vector Clustering," J. Machine Learning Research, no. 2, pp. 125-137, 2001.
22. L. Zhuang and H. Dai, "Parameter Optimization of Kernel-based One-class Classifier on Imbalance Learning," J. Computers, vol. 1, no. 7, pp. 32-40, 2006.
23. L. Zhuang and H. Dai, "Parameter Optimization of Kernel-based One-class Classifier on Imbalance Text Learning," Lecture Notes in Artificial Intelligence, Springer, vol. 4099, pp. 434-443, 2006.
24. K. Kira and L. Rendell, "A practical approach to feature selection," in Proc. Ninth Int. Workshop on Machine Learning, pp. 249-256, 1992.
25. M. Kubat and S. Matwin, "Addressing the curse of imbalanced training sets: one-sided selection," in Proc. 14th Int. Conference on Machine Learning, pages 179-186. Morgan Kaufmann, 1997.