Abstract
This paper investigates the advantages of methods based on Neural Network Classifier Ensembles - sets of neural networks working in a cooperative way to achieve a consensus decision- in the solution of the lithology recognition problem, a common task found in the petroleum exploration field. Classifier ensembles (Committees) are developed here in two stages: first, by applying procedures for creating complementary networks, i.e., networks that are individually accurate but cause distinct misclassifications; second, by applying a combining method to those networks outputs. Among the procedures for creating committee members, the Driven Pattern Replication (DPR) was chosen for the experiments, along with the ARC-X4 technique. With respect to the available combining methods, Averaging and Fuzzy Integrals were selected. All these choices were based on previous work in the field. This paper proves the effectiveness of applying ensembles in the recognition of geological facies and suggests algorithms that might be successfully applied to others classification problems.
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References
Doveton, J. H., Log Analysis of Subsurface Geology: Concepts and Computer Methods, 1986, John Wiley & Sons, 1986.
Saggaf, M. M., I Marhoon, M., and Toksöz, M. N., “Seismic facies mapping by competitive neural networks“, SEG/San Antonio 2001, San Antonio, 2001, CD-ROM.
Ford, D. A., Kelly, M. C, “Using Neural Networks to Predict Lithology from Well Logs”, SEG/San Antonio 2001, San Antonio, 2001, CD-ROM.
Taner, M. T., Walls, J. D., Smith, M., Taylor, G., Carr, M. B., Dumas, D., “Reservoir Characterization by Calibration of Self-Organized Map Clusters”, SEG/San Antonio 2001, San Antonio, 2001, CD-ROM.
J. Kittler, M. Hatef, R. P. W. Duin and J. Matas, “On combining classifiers”, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (1998), pp. 226–239.
L. Breiman, “Combining predictors”, in Combining Artificial Neural Nets: Ensemble and Modular Multi-Net System — Perspectives in Neural Computing, ed. A. J. C. Sharkey, Springer Verlag, 1999, pp. 31–51.
K. Hansen, and P. Salamon, “Neural network ensembles”, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990), pp. 993–1001.
Y. Liu and X. Yao, “Evolutionary ensembles with negative correlation learning”, IEEE Transactions on Evolutionary Computation, 4 (2000), pp. 380–387.
D. Opitz and R. Maclin, “Popular ensemble methods: an empirical study”, Journal of Artificial Intelligence Research 11 (1999), pp. 169–198.
dos Santos, R.O.V., Vellasco, M. M. B. R., Feitosa, R. Q., Simões, M., and Tanscheit, R., “An application of combined neural networks to remotely sensed images”, Proceedings of the 9th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, Pilsen, Czech Republic, 2001, pp. 87–92.
N. Ueda, “Optimal linear combination of neural networks for improving classification performance”, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000), pp. 207–215.
dos Santos, R.O.V., Combining MLP Neural Networks in Classification Problems, MSc dissertation, Electrical Engineering Department, PUC-Rio, 2001, 105 pages (in Portuguese).
L. Breiman, “Bias, variance and arcing classifiers”, Technical Report 460, University of California, Berkeley, CA.
G. A. Shafer, A Mathematical Theory of Evidence, Princeton University Press, 1976.
G. Klir and T. Folger, Fuzzy Sets, Uncertainty and Information, Prentice-Hall, 1988.
M. Sugeno, “Fuzzy measures and fuzzy integrals: a survey”, in Fuzzy Automata and Decision Processes, North Holland, Amsterdam, 1977, pp. 89–102.
R. Kohavi, “A study of cross-validation and bootstrap for accuracy estimation and model selection”, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1995, pp. 1137–1145.
S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, New Jersey, 1999.
S. K. Kachigan, Multivariate Statistical Analysis: a Conceptual Introduction, Radius Press, New York, 1991.
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dos Santos, R.V., Artola, F., da Fontoura, S., Vellasco, M. (2002). Lithology Recognition by Neural Network Ensembles. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_29
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DOI: https://doi.org/10.1007/3-540-36127-8_29
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