›› 2008, Vol. 29 ›› Issue (4): 943-948.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Classification and prediction of rockburst using AdaBoost combination learning method

GE Qi-fa1, FENG Xia-ting1,2   

  1. 1. School of Resources & Civil Engineering, Northeastern University, Shenyang 110004, China; 2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China
  • Received:2006-03-30 Online:2008-04-10 Published:2013-07-10

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Abstract: Rockburst is one of the most important geologic hazards. To solve the problem of classification and prediction of rockburst, the main factors of rockburst occurred and the effective classification methods should be considered., A new method is proposed based on combination of artificial neural networks (ANN) classifiers as weak classifiers by using AdaBoost algorithm in data mining. The AdaBoost-ANN models are established. Overcoming the instability of single classifier, the models can give more accurate and stable classification for the novel conditions. The results show that this method is reliable, constrictive and promising.

Key words: rockburst, classification, data mining, AdaBoost, ANN

CLC Number: 

  • TU 324
  • Please send e-mail to pingzhou3@126.com if you would like to read full paper in English for free. Parts of our published papers have English translations.
  • Please send e-mail to pingzhou3@126.com if you would like to read full paper in English for free. Parts of our published papers have English translations.
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