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《Chinese Journal of Geophysics》 2009-07
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Discrimination of nuclear explosions and earthquakes using the nearest support vector feature line fusion classification algorithm

LI Xi-Hai,LIU Gang,LIU Dai-Zhi,QIN Qing-QiangXi'an Research Institute of High Technology,Xi'an 710025,China  
Statistical pattern recognition is an important method to distinguish nuclear explosions from natural earthquakes, but because the number of nuclear explosions is few and this limits the generalization performance of the nuclear explosions' classifier, and finally affects the classification performance for the nuclear explosions. To improve the classification performance of the classifier, a nearest support vector feature line algorithm is introduced to expand the virtual nuclear explosions samples and improve the generalization performance correspondingly. However, this new algorithm can not distinguish nuclear explosions from natural earthquakes more accurately, and even is not as good as the nearest feature line algorithm. Theoretical analysis finds that the support vector set is not a good representation of the train set and the ratio of support vector plays an important role to the classifier. To improve the recognition result with the role of support vector ratio, support vector machine is integrated with nearest support vector feature line method by support vector ratio, and then a nearest support vector feature line fusion classification algorithm is presented, and experimental results and corresponding analysis show that this novel algorithm is more appropriate to the classification of underground nuclear explosions and natural earthquakes, and the accurate classification ratio is 90.3% on the data set of nuclear explosions and natural earthquakes, and moreover this classification ratio is higher than support vector machine and nearest feature line.
【Fund】: 国家自然科学基金项目(60705001、40274044);; 中国博士后科学基金(20080430234);; 第二炮兵工程学院科技创新基金资助.
【CateGory Index】: P315
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