LS-SVR method of ore grade estimation in Solwara 1 region with missing data

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology)

Volumn 42, Issue SUPPL. 2, 2011, Pages 147-155

  Zhang, Xu Nan ^a   Song, Shi Ji ^a   Li, Jia Biao ^b   Zhou, Ning ^c  

^a TSINGHUA UNIVERSITY   (China)

^b Ministry of Natural Resources of the People's Republic of China   (China)

^c China Ocean Mineral Resources R and D Association   (China)

Author keywords

Genetic algorithm; Grade estimation; LS SVR; Missing data; WKNN

Indexed keywords

ACCURATE ESTIMATION; DATA PREPROCESSING; DATA SEGMENTATION; ESTIMATION PROBLEM; GEOSTATISTICAL TECHNIQUES; INVERSE DISTANCE WEIGHT; K-NEAREST NEIGHBORS; KRIGING; LEAST SQUARES SUPPORT VECTOR REGRESSION; LS-SVR; MISSING DATA; MISSING VALUES; NEURAL NETWORK METHOD; NON-LINEAR METHODS; ORDINARY KRIGING; ORE GRADES; RARE METALS; RBF NEURAL NETWORK; SEA FLOOR; WKNN;

ESTIMATION; GENETIC ALGORITHMS; INTERPOLATION; MINERAL RESOURCES; NEURAL NETWORKS; ORES;

LEAST SQUARES APPROXIMATIONS;

EID: 82055194240     PISSN: 16727207     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Tutmez B. An uncertainty oriented fuzzy methodology for grade estimation[J]. Computers and Geosciences, 2007, 33(2): 280-288.
2. Samanta B. Radial basis function network for ore grade estimation[J]. Natural Resources Research, 2010, 19(2): 91-102.
3. Yama B R, Lineberry G T. Artificial neural network application for a predictive task in mining[J]. Mining Eng, 1999, 51(2): 59-64.
4. Samanta B, Bandopadhyay S, Ganguli R, et al. Data segmentation and genetic algorithms for sparse data division in nome placer gold grade estimation using neural network and geostatistics[J]. Mining Explor Geol, 2002, 11(1-4): 69-76.
5. Wu X, Zhou Y. Reserve estimation using neural network techniques[J]. Comput Geosci, 1993, 19(4): 567-575.
6. Samanta B, Bandopadhyay S. Construction of a radial basis function network using an evolutionary algorithm for grade estimation in a placer gold deposit[J]. Computers & Geosciences, 2009, 35(8): 1592-1602.
7. Anazi A F A L, Gates I D. Support vector regression for porosity prediction in a heterogeneous reservoir: A comparative study[J]. Computers & Geosciences, 2010, 36(2): 1494-1503.
8. Rajasekaran S, Gayathri S, Lee T L. Support vector regression methodology for storm surge predictions[J]. Ocean Engineering, 2008, 35(16): 1578-1587.
9. Suykens J A K, Vandewalle J. Least squares support vector machine classiers[J]. Neural Processing Letters, 1999, 9(3): 293-300.
10. Burges C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2: 121-167.
11. Smola A J, Schölkopf B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3): 199-222.
12. Gencoglu M T, Uyar M. Prediction of flashover voltage of insulators using least squares support vector machines[J]. Expert Systems with Applications, 2009, 36: 10789-10798.
13. García-Laencina P J, Sancho-Gómez J L, Figueiras-Vidal A R. Pattern classification with missing data: A review[J]. Neural Comput & Applic, 2010, 19(2): 263-282.
14. Jerez J M, Molina I, García-Laencina P J, et al. Missing data imputation using statistical and machine learning methods in a real breast cancer problem[J]. Artificial Intelligence in Medicine, 2010, 50(2): 105-115.
15. Chatterjee S, Laudatto M, Lunch L A. Genetic algorithms and their statistical applications: An introduction[J]. Computational Statistics & Data Analysis, 1996, 22(6): 633-651.