naa a22 4500 51075449X CHVBK 20180411083117.0 cr unu---uuuuu 180411e20130801xx s 000 0 eng 10.1007/s13131-013-0337-x doi (NATIONALLICENCE)springer-10.1007/s13131-013-0337-x Robust LS-SVM regression for ore grade estimation in a seafloor hydrothermal sulphide deposit [Elektronische Daten] [Xunan Zhang, Shiji Song, Jiabiao Li, Cheng Wu] Due to the geological complexities of ore body formation and limited borehole sampling, this paper proposes a robust weighted least square support vector machine (LS-SVM) regression model to solve the ore grade estimation for a seafloor hydrothermal sulphide deposit in Solwara 1, which consists of a large proportion of incomplete samples without ore types and grade values. The standard LS-SVM classification model is applied to identify the ore type for each incomplete sample. Then, a weighted K-nearest neighbor (WKNN) algorithm is proposed to interpolate the missing values. Prior to modeling, the particle swarm optimization (PSO) algorithm is used to obtain an appropriate splitting for the training and test data sets so as to eliminate the large discrepancies caused by random division. Coupled simulated annealing (CSA) and grid search using 10-fold cross validation techniques are adopted to determine the optimal tuning parameters in the LS-SVM models. The effectiveness of the proposed model by comparing with other well-known techniques such as inverse distance weight (IDW), ordinary kriging (OK), and back propagation (BP) neural network is demonstrated. The experimental results show that the robust weighted LS-SVM outperforms the other methods, and has strong predictive and generalization ability. The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg, 2013 weighted LS-SVM nationallicence grade estimation nationallicence incomplete samples nationallicence data division nationallicence Zhang Xunan Department of Automation, Tsinghua University, 100084, Beijing, China aut Song Shiji Department of Automation, Tsinghua University, 100084, Beijing, China aut Li Jiabiao Second Institute of Oceanography, State Oceanic Administration, 310012, Hangzhou, China aut Wu Cheng Department of Automation, Tsinghua University, 100084, Beijing, China aut Acta Oceanologica Sinica Springer Berlin Heidelberg 32/8(2013-08-01), 16-25 0253-505X 32:8<16 2013 32 13131 https://doi.org/10.1007/s13131-013-0337-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s13131-013-0337-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Xunan Department of Automation, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 700 1- Song Shiji Department of Automation, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 700 1- Li Jiabiao Second Institute of Oceanography, State Oceanic Administration, 310012, Hangzhou, China aut NATIONALLICENCE 700 1- Wu Cheng Department of Automation, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 773 0- Acta Oceanologica Sinica Springer Berlin Heidelberg 32/8(2013-08-01), 16-25 0253-505X 32:8<16 2013 32 13131 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer