基于克隆选择原理的核爆地震特征选择方法
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摘要
为了解决核爆地震自动识别中最优特征子集的选择问题,根据克隆选择原理,提出了一种过滤与封装相结合的特征选择方法.该方法融合了封装式与过滤式特征选择方法的优点,利用局部化的类别可分性判据来处理核爆地震样本的多峰分布问题,通过设定独立的记忆抗体能够保证最终结果是搜索过的最佳特征组合,并且可以处理设定和不设定最优特征子集维数两种情况下的特征选择问题.首先通过UCI数据集中呈多峰分布的玻璃数据验证了该特征选择方法的有效性,进而将其应用到核爆地震特征选择中.核爆地震特征选择实验结果表明,该方法不仅有效地降低了特征空间的维数,而且使分类精度提高了2个百分点,与封装式特征选择方法相比,该方法的计算复杂度大为降低.
A filter-wrapper feature selection algorithm is proposed based on the clonal selection principle to select the optimal feature subset in the automatic classification of nuclear explosions and earthquakes.This algorithm possesses the merits of both filter and wrapper.It utilizes local class separability criterion to deal with multimodal data of nuclear explosions and earthquakes.A separate memory antibody is set which can guarantee the output is the best feature combination among that was searched and can deal with both cases under which the optimal dimensionality is specified in advance or not.Firstly,the effectiveness is validated using glasses data of UCI whose distribution is multimodal,and then it is applied to feature selection of nuclear explosions and earthquakes.The experimental results on seismic data show that the proposed algorithm reduces feature dimensionality effectively while improves the recognition accuracy by 2 percent. Moreover,the computation complexity is less than that of wrapper.
A filter-wrapper feature selection algorithm is proposed based on the clonal selection principle to select the optimal feature subset in the automatic classification of nuclear explosions and earthquakes.This algorithm possesses the merits of both filter and wrapper.It utilizes local class separability criterion to deal with multimodal data of nuclear explosions and earthquakes.A separate memory antibody is set which can guarantee the output is the best feature combination among that was searched and can deal with both cases under which the optimal dimensionality is specified in advance or not.Firstly,the effectiveness is validated using glasses data of UCI whose distribution is multimodal,and then it is applied to feature selection of nuclear explosions and earthquakes.The experimental results on seismic data show that the proposed algorithm reduces feature dimensionality effectively while improves the recognition accuracy by 2 percent. Moreover,the computation complexity is less than that of wrapper.
引文
1) 有关不同形式的基于类内类间离散度矩阵的类别可分性判据的比较及局部J_3准则的详细内容可参看:韩绍卿,李夕海,刘代志.基于局部类内类间离散度矩阵的类别可分性判据.http://www.paper.edu.cn/paper.php?serial_number=200910-479,2009
2) AsuncionA,Newman D J.UCI Machine Learning Repository[http://www.ics.uci.edu/～mlearn/ML Repository.html].Irvine,C A:Universityof California,School of Information and Computer Science,2007
[1] 李夕海,刘刚,刘代志等.基于最近邻支撑向量特征线融合算法的核爆地震识别.地球物理学报,2009,52(7) :1816～1824 Li X H,Liu G,Liu D Z,et al.Discrimination of nuclear explosions and earthquakes using the nearest support vector feature line fusion classification algorithm.Chinese J.Geophys.(in Chinese),2009,52(7) :1816～1824
[2] 沈萍,郑治真.瞬态谱在地震与核爆识别中的应用.地球物理学报,1999,42(2) :231～240 Shen P,Zheng Z Z.Application of transient spectrum to discrimination of nuclear explosions and earthquakes.Chinese J.Geophys.(in Chinese),1999,42(2) :231～240
[3] 杨选辉,沈萍,刘希强等.地震与核爆识别的小波包方法.地球物理学报,2005,48(1) :148～156 Yang X H,Shen P,Liu X Q,et al.Application of method of spectral component ratio of wavelet-packets to discrimination between earthquakes and nuclear explosions.Chinese J.Geophys.(in Chinese),2005,48(1) :148～156
[4] 李夕海,刘代志,张斌等.序优化在核爆地震特征选择中的应用研究.兵工学报,2005,26(4) :496～499 Li X H,Liu D Z,Zhang B,et al.Feature selection of seismic pattern recognition of nuclear explosions based on ordinal optimization.Acta Armament Arii(in Chinese),2005,26(4) :496～499
[5] Orlic N,Loncaric S.Earthquake-explosion discrimination using genetic algorithm based boosting approach.Computers & Geosciences,2010,36(2) :179～185
[6] Dash M,Liu H.Feature selection for classification.Intelligent Data Analysis,1997,(1) :131～156
[7] Siedlecki W,Sklansky J.On automatic feature selection.International Journal of Pattern Recognition and Artificial Intelligence,1988,2(2) :197～220
[8] Kirkpatrick S,Gelatt Jr C D,Vecchi M P.Optimization by simulated annealing.Science,1983,220:671～680
[9] Gutjahr W J.A graph-based ant system and its convergence.Future Generation Computing Systems,2000,16:873～888
[10] De Castro L N,Von Zuben F J.Learning and optimization using the clonal selection principle.IEEE Trans on Evolutionary Computation,2002,6(3) :239～251
[11] 张向荣,焦李成.基于免疫克隆选择算法的特征选择.复旦大学学报(自然科学版),2004,43(5) :926～929 Zhang X R,Jiao L C.Feature selection based on immune clonal selection algorithm.Journal of Fudan University(Natural Science)(in Chinese),2004,43(5) :926～929
[12] 孙即详等.现代模式识别.长沙:国防科技大学出版社,2002 Sun J X et al.Modern Pattern Recognition(in Chinese).Changsha:National University of Defense Technology Press,2002
[13] 边肇祺,张学工编著.模式识别(第二版).北京:清华大学出版社,1999 Bian Z Q,Zhang X G.Pattern Recognition(2~(nd)Edition)(in Chinese).Beijing:Tsinghua University Press,1999
[14] Zelnik-Manor L,Perona P.Self-tuning spectral clustering.In:Saul L K,Weiss Y,Bottou L eds.Advances in Neural Information Processing Systems.Cambridge,MA:MIT Press,2005,17:1601～1608
2) AsuncionA,Newman D J.UCI Machine Learning Repository[http://www.ics.uci.edu/～mlearn/ML Repository.html].Irvine,C A:Universityof California,School of Information and Computer Science,2007
[1] 李夕海,刘刚,刘代志等.基于最近邻支撑向量特征线融合算法的核爆地震识别.地球物理学报,2009,52(7) :1816～1824 Li X H,Liu G,Liu D Z,et al.Discrimination of nuclear explosions and earthquakes using the nearest support vector feature line fusion classification algorithm.Chinese J.Geophys.(in Chinese),2009,52(7) :1816～1824
[2] 沈萍,郑治真.瞬态谱在地震与核爆识别中的应用.地球物理学报,1999,42(2) :231～240 Shen P,Zheng Z Z.Application of transient spectrum to discrimination of nuclear explosions and earthquakes.Chinese J.Geophys.(in Chinese),1999,42(2) :231～240
[3] 杨选辉,沈萍,刘希强等.地震与核爆识别的小波包方法.地球物理学报,2005,48(1) :148～156 Yang X H,Shen P,Liu X Q,et al.Application of method of spectral component ratio of wavelet-packets to discrimination between earthquakes and nuclear explosions.Chinese J.Geophys.(in Chinese),2005,48(1) :148～156
[4] 李夕海,刘代志,张斌等.序优化在核爆地震特征选择中的应用研究.兵工学报,2005,26(4) :496～499 Li X H,Liu D Z,Zhang B,et al.Feature selection of seismic pattern recognition of nuclear explosions based on ordinal optimization.Acta Armament Arii(in Chinese),2005,26(4) :496～499
[5] Orlic N,Loncaric S.Earthquake-explosion discrimination using genetic algorithm based boosting approach.Computers & Geosciences,2010,36(2) :179～185
[6] Dash M,Liu H.Feature selection for classification.Intelligent Data Analysis,1997,(1) :131～156
[7] Siedlecki W,Sklansky J.On automatic feature selection.International Journal of Pattern Recognition and Artificial Intelligence,1988,2(2) :197～220
[8] Kirkpatrick S,Gelatt Jr C D,Vecchi M P.Optimization by simulated annealing.Science,1983,220:671～680
[9] Gutjahr W J.A graph-based ant system and its convergence.Future Generation Computing Systems,2000,16:873～888
[10] De Castro L N,Von Zuben F J.Learning and optimization using the clonal selection principle.IEEE Trans on Evolutionary Computation,2002,6(3) :239～251
[11] 张向荣,焦李成.基于免疫克隆选择算法的特征选择.复旦大学学报(自然科学版),2004,43(5) :926～929 Zhang X R,Jiao L C.Feature selection based on immune clonal selection algorithm.Journal of Fudan University(Natural Science)(in Chinese),2004,43(5) :926～929
[12] 孙即详等.现代模式识别.长沙:国防科技大学出版社,2002 Sun J X et al.Modern Pattern Recognition(in Chinese).Changsha:National University of Defense Technology Press,2002
[13] 边肇祺,张学工编著.模式识别(第二版).北京:清华大学出版社,1999 Bian Z Q,Zhang X G.Pattern Recognition(2~(nd)Edition)(in Chinese).Beijing:Tsinghua University Press,1999
[14] Zelnik-Manor L,Perona P.Self-tuning spectral clustering.In:Saul L K,Weiss Y,Bottou L eds.Advances in Neural Information Processing Systems.Cambridge,MA:MIT Press,2005,17:1601～1608
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