Journal of Jilin University(Earth Science Edition) ›› 2019, Vol. 49 ›› Issue (2): 611-620.doi: 10.13278/j.cnki.jjuese.20180016

Previous Articles    

Application of Support Vector Machine Based on Decision Tree Feature Extraction in Lithology Classification

Han Qidi1, Zhang Xiaotong2, Shen Wei1   

  1. 1. School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China;
    2. China Land Surveying and Planning Institute, Beijing 100035, China
  • Received:2018-01-23 Online:2019-03-26 Published:2019-03-28
  • Supported by:
    Supported by National Natural Science Foundation of China (41172302, 40672196)

PDF (PC)

318

Abstract: Support vector machine is a kind of black box model,and its feature cannot be selected directly when learning model;while decision tree model has the ability of feature selection during the process of recursive creation.For lithology classification,we combined decision tree with support vector machine.In consideration with the importance of the features,we used the tree height to extract the features after the decision tree establishment,and furthermore,we used the features with higher classification ability to fed into the support vector machine.The results show that the feature extraction of decision tree can reduce the input characteristics,so this,in turn,makes the SVM model more stable and accurate through controlling the complexity of the model effectively.The accuracy of test set of the model can be increased by more than 10%.

Key words: support vector machine, decision tree, feature extraction, lithology classification

CLC Number: 

  • P58
[1] 李航.统计学习方法[M].北京:清华大学出版社,2012. Li Hang.Statistical Learning Method[M].Beijing:Tsinghua University Press,2012.
[2] 于代国,孙建孟,王焕增,等.测井识别岩性新方法:支持向量机方法[J].大庆石油地质与开发,2005,24(5):93-95. Yu Daiguo,Sun Jianmeng,Wang Huanzeng,et al.A New Method of Logging Recognition Lithology:Support Vector Machine Method[J].Daqing Petroleum Geology and Development,2005,24(5):93-95.
[3] 周继宏,袁瑞.基于支持向量机的复杂碎屑岩储层岩性识别[J].石油天然气学报,2012,34(7):72-75. Zhou Jihong,Yuan Rui.Lithology Identification of Complex Clastic Rock Reservoirs Based on Support Vector Machine[J]. Journal of Oil and Gas Technolog,2012,34(7):72-75.
[4] 张翔,肖小玲,严良俊,等.基于模糊支持向量机方法的岩性识别[J].石油天然气学报(江汉石油学院学报),2009,31(6):115-118. Zhang Xiang,Xiao Xiaoling,Yan Liangjun,et al.Lithology Identification Based on Fuzzy Support Vector Machine[J].Journal of Oil and Gas Technolog,2009,31(6):115-118.
[5] 李洪奇,谭锋奇,许长福,等.基于决策树方法的砾岩油藏岩性识别[J].测井技术,2010,34(1):16-21. Li Hongqi,Tan Fengqi,Xu Changfu,et al.Lithology Identification of Conglomerate Reservoir Based on Decision Tree Method[J].Well Logging Technology,2010,34(1):16-21.
[6] 石广仁.支持向量机在裂缝预测及含气性评价应用中的优越性[J].石油勘探与开发,2008,35(5):589-594. Shi Guangren.Superiorities of Support Vector Machine in Fracture Prediction and Gassinesse Valuation[J].Petroleum Exploration and Development,2008,35(5):589-594.
[7] 桑吉夫·库尔卡尼,吉尔伯特·哈曼.统计学习理论基础[M].肖忠祥,闫效莺,段沛沛,等译.北京:机械工业出版社,2017. Kulkarni S,Harman G.An Elementary Introduction to Statistical Learining Theory[M].Translated by Xiao Zhongxiang,Yan Xiaoying,Duan Peipei,et al.Beijing:Machinery Industry Press,2017.
[8] 王建国,董泽宇,张文兴,等.基于回归树的支持向量机规则提取及应用[J].计算机工程与应用,2017,53(6):236-240. Wang Jianguo,Dong Zeyu,Zhang Wenxing,et al.Rule Extraction of Support Vector Machine Based on Regression Tree and Application[J].Computer Engineering and Applications,2017,53(6):236-240.
[9] Barakat N,Bradley A P.Rule Extraction from Support Vector Machines:A Review[J].Neurocomputing,2010,74(5):178-190.
[10] 温小霓,蔡汝骏.分类与回归树及其应用研究[J].统计与决策,2007(23):14-16. Wen Xiaoni,Cai Rujun.Classification and Regression Tree and Its Application Research[J].Statistics and Decision,2007(23):14-16.
[11] 谢益辉.基于R软件rpart包的分类与回归树应用[J].统计与信息论坛,2007,22(5):67-70. Xie Yihui.Classification and Regression Tree Application Based on R Software Rpart Package[J].Forum on Statistics and Information,2007,22(5):67-70.
[12] 周志华.机器学习[M].北京:清华大学出版社,2016:1-415. Zhou Zhihua.Machine Learning[M].Beijing:Tsinghua University Press,2016:1-415.
[13] 范淼,李超.Python机器学习及实践[M].北京:清华大学出版社,2016:1-180. Fan Miao,Li Chao.Python Machine Learning and Practice[M].Beijing:Tsinghua University Press,2016:1-180.
[14] 张冰,郭智奇,徐聪,等.基于岩石物理模型的页岩储层裂缝属性及各向异性参数反演[J].吉林大学学报(地球科学版),2018,48(4):1244-1252. Zhang Bing,Guo Zhiqi,Xu Cong,et al.Fracture Properties and Anisotropic Parameters Inversion of Shales Based on Rock Physics Model[J].Journal of Jilin University (Earth Science Edition),2018,48(4):1244-1252.
[15] 杨震宇.基于机器学习的分类算法研究[J].科学中国人,2017,2:22-25. Yang Zhenyu.Research on Classification Algorithm Based on Machine Learning[J].Scientific Chinese,2017,2:22-25.
[16] 丁世飞,齐丙娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1):2-10. Ding Shifei,Qi Bingjuan,Tan Hongyan.An Overview on Theory and Algorithm of Support Vector Machines[J].Journal of University of Electronic Science and Technology of China,2011,40(1):2-10.
[17] 冷强奎,李玉鑑.使用SVM和二叉树结构的分片线性分类器[J].中国科技论文,2015,10(2):164-168. Leng Qiangkui,Li Yujian.A Piecewise Linear Classifier Using SVM and two Forked Tree Structure[J].China Sciencepaper,2015,10(2):164-168.
[18] 石广仁.支持向量机在多地质因素分析中的应用[J].石油学报,2008,29(2):195-198. Shi Guangren.Application of Support Vector Machine to Multi-Geological-Factor Analysis[J].Acta Petrolei Sinica,2008,29(2):195-198.
[19] Janez D,Dale S.Statistical Comparisons of Classifiers over Multiple DataSets[J].Journal of Machine Learning Research,2006,7(1):1-30.
[20] Zhang M,Zhou Z.A Review on Multi-Label Learning Algorithms[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(8):1819-1837.
[21] Yoonkyung L,Yi L,Grace W.Multicategory Support Vector Machines:Theory and Application to the Classification of Microarray Data and Satellite Radiance Data[J].Journal of the American Statistical Association,2004,99:67-81.
[22] Fan R E,Chang K W,Hsieh C J,et al.LIBLINEAR:A Library for Large Linear Classification[J].Journal of Machine Learning Research,2008,9:1871-1874.
[23] Shwartz S S,Singer Y,Srebro N,et al.Pegasos:Primal Estimated Sub-Gradient Solver for SVM[J].Mathematical Programming,2011,127(1):3-30.
[24] Bouchaffra D,Vitae A,Cheriet M.Machine Learning and Pattern Recognition Models in Change Detection[J].Pattern Recognition,2015,48(3):613-615.
[25] Collins M,Schapire R E,Singer Y.Logistic Regression,Ada Boost and Bregman Distances[J].Machine Learning,2002,48(1):235-285.
[26] Canu S,Smola A.Kernel Methods and the Exponential Family[J].Neurocomputing,2006,69(7):714-720.
[27] Tsochantaridis I,Joachims T,Hofmann T,et al.Large Margin Methods for Structured and Interdependent Output Variables[J].Journal of Machine Learning Research,2005,1:1453-1484.
[28] Chang C C,Lin C J.LIBSVM:A Library for Support Vector Machines[J/OL].ACM Transactions on Intelligent Systems and Technology,2011,2(3).http://dx.doi.org/10.1145/1961189.1961199.
[1] Xu Shouyu, Lu Yan, Wang Ya. Study on Flow Units of Turbidite Fan Low Permeability Reservoir Based on Support Vector Machine [J]. Journal of Jilin University(Earth Science Edition), 2018, 48(5): 1330-1341.
[2] Wang Mingchang, Zhang Xinyue, Zhang Xuqing, Wang Fengyan, Niu Xuefeng, Wang Hong. GF-2 Image Classification Based on Extreme Learning Machine [J]. Journal of Jilin University(Earth Science Edition), 2018, 48(2): 373-378.
[3] Lu Wenxi, Guo Jiayuan, Dong Haibiao, Zhang Yu, Lin Lin. Evaluating Mine Geology Environmental Quality Using Improved SVM Method [J]. Journal of Jilin University(Earth Science Edition), 2016, 46(5): 1511-1519.
[4] Yang Xuefeng, Wang Xuemei, Mao Donglei. Mapping Land Use and Land Cover Through MISR Multi-Angle Imagery in the Lower Tarim River [J]. Journal of Jilin University(Earth Science Edition), 2016, 46(2): 617-626.
[5] Zhou Linfei, Chen Qixin, Cheng Qian, Zhang Jing. Remote Sensing Classification Information Extraction Based on Rough Set Theory [J]. Journal of Jilin University(Earth Science Edition), 2015, 45(4): 1246-1256.
[6] Mou Dan, Wang Zhuwen, Huang Yulong, Xu shi, Zhou Dapeng. Application of Least Squares Support Vector Machine to Lithology Identification: Taking Intermediate/Basaltic Rocks of Liaohe Basin as an Example [J]. Journal of Jilin University(Earth Science Edition), 2015, 45(2): 639-648.
[7] YANG Jia-jia, JIANG Qi-gang, ZHAO Jing, XU Yan, MENG Xiang-chong. Standard Mineral Quantitative Calculation Based on the Improved SVM and Hyperspectral Remote Sensing [J]. J4, 2012, 42(3): 864-871.
[8] NIU Rui-qing, PENG Ling, YE Run-qing, WU Xue-ling. Landslide Susceptibility Assessment Based on Rough Sets and Support Vector Machine [J]. J4, 2012, 42(2): 430-439.
[9] XU Chang-fu, LI Xiong-yan, TAN Feng-qi, YU Hong-yan, LI Hong-qi. Task-Driven Data Mining and Its Application of Identifying the Low Resistivity Oil Reservoir[ [J]. J4, 2012, 42(1): 39-46.
[10] WANG Li-hua, ZHOU Yun-xuan, TIAN Bo. Detecting Coral Reefs at Dongsha Atoll Using Landsat TM and ETM+Images [J]. J4, 2011, 41(5): 1630-1637.
[11] LI Xiu-zhen, KONG Ji-ming, WANG Cheng-hua. Application of Multi-Classification Support Vector Machine in the Identifying of Landslide Stability [J]. J4, 2010, 40(3): 631-637.
[12] HUANG Ying, ZHOU Yun-xuan, WU Wen, KUANG Run-yuan, LI Xing. Shanghai Urban Wetland Extraction and Classification with Remote Sensed Imageries Based on A Decision Tree Model [J]. J4, 2009, 39(6): 1156-1162.
[13] CHENG Bin, JIANG Qi-gang, ZHOU Yun-xuan，ZHAN Shao-bin. Decision Tree Based on ASTER Image Classification and Its Application [J]. J4, 2007, 37(1): 179-0184.
[14] XU Hong-min, YANG Tian-xing. Evaluation of Lake Water Quality Based on Classification Algorithms of Support Vector Machines [J]. J4, 2006, 36(04): 570-573.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Earth Science Edition), All Rights Reserved.
Tel: +86-431-88502374；13756165364　E-mail: xuebao1956@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd