针对基因表达数据样本少,维数高的特点,尤其是在样本分型缺乏先验知识的情况下,结合自组织特征映射的优点提出了基于代表熵的双向聚类算法。该算法首先通过自组织特征映射网络(SOM)对基因聚类,根据波动系数挑选特征基因。然后根据代表...针对基因表达数据样本少,维数高的特点,尤其是在样本分型缺乏先验知识的情况下,结合自组织特征映射的优点提出了基于代表熵的双向聚类算法。该算法首先通过自组织特征映射网络(SOM)对基因聚类,根据波动系数挑选特征基因。然后根据代表熵的大小判断基因聚类的好坏,并确定网络的神经元个数。最后采用FCM(Fuzzy C Means)聚类算法对挑选出的特征基因集进行样本分型。将该算法用于两组公开的基因表达数据集,实验结果表明该算法在降低特征维数的同时,得出了较高的聚类准确率。展开更多
This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster syste...This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster system with complicated and nonlinear characteristics, a non-linear information dynamics mode is introduced based on the maximum flux principle during modeling process to study the integrated risk assessment of complex disaster system. Based on the non-equilibrium statistical mechanics method, a stochastic evolution equation of this system is established. The integrated risk assessment of complex disaster system can be achieved by giving reasonable weights of each evaluation index to stabilize the system. The new model reveals the formation pattern of risk grade and the dynamics law of evolution. Meanwhile, a method is developed to solve the dynamics evolution equations of complex system through the self-organization feature map algorithm. The proposed method has been used in complex disaster integrated risk assessment for 31 provinces, cities and autonomous regions in China mainland. The results have indicated that the model is objective and effective.展开更多
文摘针对基因表达数据样本少,维数高的特点,尤其是在样本分型缺乏先验知识的情况下,结合自组织特征映射的优点提出了基于代表熵的双向聚类算法。该算法首先通过自组织特征映射网络(SOM)对基因聚类,根据波动系数挑选特征基因。然后根据代表熵的大小判断基因聚类的好坏,并确定网络的神经元个数。最后采用FCM(Fuzzy C Means)聚类算法对挑选出的特征基因集进行样本分型。将该算法用于两组公开的基因表达数据集,实验结果表明该算法在降低特征维数的同时,得出了较高的聚类准确率。
基金supported by the National Twelfth Five-year Technology Support Projects of China (Grant Nos. 2009BAJ28B04, 2011BAK07B01,2011BAJ08B03, and 2011BAJ08B05)the National Natural Science Foundation of China (Grant No. 51208017)+1 种基金Beijing Postdoctoral Research Foundation (Grant No. 2012ZZ-17)China Postdoctoral Science Foundation Funded Project (Grant No. 2011M500199)
文摘This paper describes a non-linear information dynamics model for integrated risk assessment of complex disaster system from an evolution perspective. According to the occurrence and evolution of natural disaster system with complicated and nonlinear characteristics, a non-linear information dynamics mode is introduced based on the maximum flux principle during modeling process to study the integrated risk assessment of complex disaster system. Based on the non-equilibrium statistical mechanics method, a stochastic evolution equation of this system is established. The integrated risk assessment of complex disaster system can be achieved by giving reasonable weights of each evaluation index to stabilize the system. The new model reveals the formation pattern of risk grade and the dynamics law of evolution. Meanwhile, a method is developed to solve the dynamics evolution equations of complex system through the self-organization feature map algorithm. The proposed method has been used in complex disaster integrated risk assessment for 31 provinces, cities and autonomous regions in China mainland. The results have indicated that the model is objective and effective.
