爆炸地震地面振动速度的随机相似性

根据实验测试结果 ,讨论了爆炸地震地面振动的随机相似现象。提出了利用速度时间历程及其功率谱密度函数判别随机相似性出现的条件。当爆炸源和所研究地面振动地点的位置确定时 ,如果功率谱密度函数满足统计相似条件 ,则速度时间历程具有随机相似性。

属性网络中相似性增强随机游走的多社区搜索

社区搜索旨在寻找与给定查询节点高度相关的个性化社区.现有社区搜索方法多面向简单网络且处理单个查询节点或假定多个查询节点来自同一社区,这种严格的假设使得算法灵活性受限.据此,提出一种在属性网络中利用查询节点随机游走路径的相似性增强的多社区搜索方法,可以有效地定位查询节点所属的多个局部目标社区.具体地,有效融合网络中高阶结构与属性信息,利用重启随机游走计算各查询节点的重要性分数向量;计算查询节点随机游走路径的相似性并设计一种相似性增强策略,使得在无监督学习中相似路径游走者彼此增强关联从而定位不同查询节点所属的多个社区结构;基于结合结构和属性的并行电导值精准查询社区.真实数据集和人工数据集的实验验证了本文方法的有效性和效率.

A random forest algorithm based on similarity measure and dynamic weighted voting

The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model.