Because most ensemble learning algorithms use the centralized model, and the training instances must be centralized on a single station, it is difficult to centralize the training data on a station. A distributed ense...Because most ensemble learning algorithms use the centralized model, and the training instances must be centralized on a single station, it is difficult to centralize the training data on a station. A distributed ensemble learning algorithm is proposed which has two kinds of weight genes of instances that denote the global distribution and the local distribution. Instead of the repeated sampling method in the standard ensemble learning, non-balance sampling from each station is used to train the base classifier set of each station. The concept of the effective nearby region for local integration classifier is proposed, and is used for the dynamic integration method of multiple classifiers in distributed environment. The experiments show that the ensemble learning algorithm in distributed environment proposed could reduce the time of training the base classifiers effectively, and ensure the classify performance is as same as the centralized learning method.展开更多
【目的】药物-靶标相互作用预测在药物重定位和药物开发方面起着至关重要的作用。【方法】提出了一种基于冗余度-相关性和交互作用结合的多特征融合算法RCI(redundancy-correlation and interaction),并结合堆叠集成分类器搭建药物靶标...【目的】药物-靶标相互作用预测在药物重定位和药物开发方面起着至关重要的作用。【方法】提出了一种基于冗余度-相关性和交互作用结合的多特征融合算法RCI(redundancy-correlation and interaction),并结合堆叠集成分类器搭建药物靶标预测模型。首先,提取药物和靶标的高维特征进行多特征融合,使用RCI算法构建非冗余的且具有相关性的交互特征子集。然后,将交互特征子集输入到由多个基学习器构成的堆叠集成分类器中进行训练。最后,对两个基准药物靶标网络进行了预测。【结果】实验结果表明,所搭建模型的准确度ACC值和AUC值均优于现有基线方法,说明所提算法的有效性。展开更多
基金the Natural Science Foundation of Shaan’xi Province (2005F51).
文摘Because most ensemble learning algorithms use the centralized model, and the training instances must be centralized on a single station, it is difficult to centralize the training data on a station. A distributed ensemble learning algorithm is proposed which has two kinds of weight genes of instances that denote the global distribution and the local distribution. Instead of the repeated sampling method in the standard ensemble learning, non-balance sampling from each station is used to train the base classifier set of each station. The concept of the effective nearby region for local integration classifier is proposed, and is used for the dynamic integration method of multiple classifiers in distributed environment. The experiments show that the ensemble learning algorithm in distributed environment proposed could reduce the time of training the base classifiers effectively, and ensure the classify performance is as same as the centralized learning method.
文摘【目的】药物-靶标相互作用预测在药物重定位和药物开发方面起着至关重要的作用。【方法】提出了一种基于冗余度-相关性和交互作用结合的多特征融合算法RCI(redundancy-correlation and interaction),并结合堆叠集成分类器搭建药物靶标预测模型。首先,提取药物和靶标的高维特征进行多特征融合,使用RCI算法构建非冗余的且具有相关性的交互特征子集。然后,将交互特征子集输入到由多个基学习器构成的堆叠集成分类器中进行训练。最后,对两个基准药物靶标网络进行了预测。【结果】实验结果表明,所搭建模型的准确度ACC值和AUC值均优于现有基线方法,说明所提算法的有效性。
