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

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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)

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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
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