Ore grade prediction using a genetic algorithm and clustering Based ensemble neural network model

Mathematical Geosciences

Volumn 42, Issue 3, 2010, Pages 309-326

  Chatterjee, Snehamoy ^a   Bandopadhyay, Sukumar ^b   Machuca, David ^c  

^a MCGILL UNIVERSITY   (Canada)

^b UNIVERSITY OF ALASKA FAIRBANKS   (United States)

^c UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Ensemble network; GA; k means clustering; Neural network; Support vector machine

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; ENSEMBLE FORECASTING; GENETIC ALGORITHM; INTERPOLATION; LEAD; MODELING; ORE DEPOSIT; ORE GRADE; PREDICTION; ZINC;

EID: 77952885518     PISSN: 18748961     EISSN: 18748953     Source Type: Journal    
DOI: 10.1007/s11004-010-9264-y     Document Type: Article

References (35)

1. Bishop CM (1998) Neural networks for pattern recognition. Oxford University Press, New York.
2. Breiman L (1996) Bagging predictors. Mach Learn 24(2): 123-140.
3. Chao L (2004) Multidimensional nonstationary maximum entropy spectral analysis by using neural net. Math Geol 31(6): 685-700.
4. Chatterjee S, Bhattacherjee A, Samanta B, Pal SK (2006) Ore grade estimation of a limestone deposit in India using an artificial neural network. Appl GIS 2(1): 2. 1-2. 20.
5. Cho S-B, Ahn J-H (2001) Speciated neural networks evolved with fitness sharing technique. Congr Evol Comput 1: 390-396.
6. David RL, Gregory JM (1999) Evaluating the use of "goodness-of-fit" measures in hydrological and hydro-climatic model validation. Water Resour Res 35(1): 233-241.
7. Devijver PA, Kittler J (1982) Pattern recognition: a statistical approach. Prentice Hall, Englewood Cliffs.
8. Dietterich TG (1998) Machine-learning research: four current directions. AI Mag 18(4): 97-136.
9. Dondeti S, Kannan K, Manavalan R (2005) Genetic algorithm optimized neural networks ensemble for estimation of mefenamic acid and paracetamol in tablets. Acta Chim Slov 52: 440-449.
10. Dowd PA, Sarac C (1994) A neural network approach to geostatistical simulation. Math Geol 26: 491-503.
11. Dutta S, Misra D, Ganguli R, Bandopadhyay S (2006) A hybrid ensemble model of kriging and neural network for ore grade estimation. Int J Surf Min Reclam Environ 20(1): 33-45.
12. Girosi F (1998) An equivalence between sparse approximation and support sector machines. Neural Comput 10: 1455-1480.
13. Goldberg DE (2002) Genetic algorithms in search, optimization, and machine learning. Pearson Education Asia, Delhi.
14. Haykins S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, New Jersey.
15. Koike K, Matsuda S, Gu B (2001) Evaluation of interpolation accuracy of neural kriging with application to temperature-distribution analysis. Math Geol 28: 421-448.
16. Koike K, Matsuda S, Suzuki T, Ohmi M (2002) Neural network-based estimation of principal metal contents in the Hokuroku district, northern Japan, for exploring Kuroko-type deposits. Nat Resour Res 11(2): 135-156.
17. Krogh A, Vedelsby J (1995) Neural network ensembles, cross-validation, and active learning. Neural Inf Process Syst 7: 231-238.
18. Li K, Huang H-K, Ye X-C, Chi L-J (2004) A selective approach to neural network ensemble based on clustering technology. In 3rd international conference on machine learning and cybernetics, 26-29th August, 2004. Shanghai, China, pp 3229-3233.
19. Liu Y, Yao X (1997) Evolving modular neural networks which generalize well. In IEEE international conference on evolutionary computation, Indianapolis, USA, pp 605-610.
20. Liu Y, Yao X (1999) Ensemble learning via negative correlation. Neural Netw 12: 1399-1404.
21. Liu Y, Yao X, Higuchi T (2000) Evolutionary ensembles with negative correlation learning. IEEE Trans Evol Comput 4(4): 380-387.
22. Matías J, Vaamonde A, Taboada J, González-Manteiga W (2004) Comparison of kriging and neural networks with application to the exploitation of a slate mine. Math Geol 36(4): 463-486.
23. Opitz DW, Shavlik JW (1996) Actively searching for an effective neural network ensemble. Connect Sci 8(3-4): 337-353.
24. Pan G, Gaard D, Moss K, Heiner T (1993) A comparison between co-kriging and ordinary kriging: case study with a polymetallic deposit. Math Geol 25(3): 377-398.
25. Rosen BE (1996) Ensemble learning using de-correlated neural networks. Connect Sci 8(3-4): 373-384. Special issue on combining artificial neural: ensemble approaches.
26. Samanta B, Bandopadhyay S, Ganguli R (2004a) Data segmentation and genetic algorithms for sparse data division in Nome placer grade estimation using neural network and geostatistics. Explor Min Geol 11: 69-76.
27. Samanta B, Bandopadhyay S, Ganguli R, Dutta S (2004b) Sparse data division using data segmentation and Kohonen network for neural network and geostatistical ore grade modeling in Nome offshore placer deposit. Nat Resour Res 13(3): 189-200.
28. Samanta B, Bandopadhyay S, Ganguli R (2006) Comparative evaluation of neural network learning algorithms for ore grade estimation. Math Geology 38(2): 175-197.
29. Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2): 197-227.
30. Sharkey A (1998) Combining artificial neural nets ensemble and modular multi-net systems. Springer, New York.
31. Soler P, Bonhomme MG (1998) Oligocene magmatic activity and associated mineralization in the polymetallic belt of central Peru. Econ Geol 83: 657-663.
32. Vapnik V (1995) The nature of statistical learning theory. Springer, New York.
33. Wu J-X, Zhou Z-H, Chen Z-Q (2001) Ensemble of GA based selective neural network ensembles. In 8th international conference on neural information processing, vol 3, pp 1477-1482.
34. Yama BR, Lineberry GT (1999) Artificial neural network application for a predictive task in mining. Min Eng 51(2): 59-64.
35. Yamamoto JK (2004) An alternative measure of the reliability of ordinary kriging estimates. Math Geol 32(4): 489-509.