%0 Journal Article
%T Behavior of disease propagation with random long-distance spreading on complex networks<br>考虑远程随机感染的复杂网络上疾病传播行为*
%A ZHU Xiao-jun
%A ZHANG Ning
%A LI Ji-ming
%A <br>朱晓军
%A 张宁
%A 李季明
%J 计算机应用研究
%D 2010
%I 
%X Classifiers based on discriminant model achieved the highest accuracy compared to other protein classification methods in remote homology detection, but all of the classifiers were troubled by imbalance training in modeling. This paper presented a protein classification based on optimization of discriminant model to further improve the classifier performance by setting different penalty coefficients for the positive and negative samples to balance the training set weights. Comparative experiments show that the method based on optimized discriminant model obtained higher accuracy, and the method can improve the performance of all classifiers based on discriminant model by optimization of the parameters.
%K small-world networks
%K scale-free networks
%K SIRS model
%K long-distance spreading
%K stationary infected density<br>小世界网络
%K 无标度网络
%K SIRS模型
%K 远程感染
%K 稳态感染密度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=7701548F6ADBD055CE3CCEA4B94639FA&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=708DD6B15D2464E8&sid=39C6C698B81939CE&eid=7EF52AFF868921C3&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=18