基于Moore-Penrose逆矩阵的选择性集成

本文提出了一种基于Moore-Penrose逆矩阵的新型选择性集成学习算法.先独立训练出一批个体学习器并为每个学习器指定一个初始权值,然后应用基于Moore-Penrose逆矩阵的算法对这些权值进行优化,最后选择权值较大的个体学习器进行最终集成.本文提出的选择性集成学习算法方法简单、易于实现,执行效率高.对8个真实数据集的实验表明,该集成学习算法相对于一般的集成学习算法,可以采用更少的学习器而获得更高的泛化能力.

Selective Ensemble Based on Moore-Penrose Pseudo-inverse

A novel selective ensemble learning algorithm is proposed based on Moore-Penrose pseudo-inverse. After individual learning machines had been trained independently, a set of initial weights were assigned to the individuals. Then an method based on Moore-Penrose pseudo-inverse was applied to optimize these weights for a minimum generalization error, and only those with high weights would be chosen for the final ensemble. Experiments show that this method is efficient in the computation complexity and easy to be implemented. Moreover, the method can achieve higher generalization ability with a much smaller ensemble size than other popular ensemble approaches.