基于成对差异性度量的选择性集成方法

有效地产生泛化能力强、差异大的个体学习器，是集成学习算法的关键。为了提高学习器的差异性和精度，文中提出一种基于成对差异性度量的选择性集成方法。同时研究一种改进方法，进一步提高方法的运算速度，且支持并行计算。最后通过使用BP神经网络作为基学习器，在UCI数据集上进行实验，并与Bagging、基于遗传算法的选择性集成(GASEN)算法进行比较。实验结果表明，该改进算法在性能上与GASEN算法相近的前提下，训练速度得到大幅提高。

Pairwise Diversity Measures Based Selective Ensemble Method

Effective generating individual learners with strong generalization ability and great diversity is the key issue of ensemble learning. To improve diversity and accuracy of learners, Pairwise Diversity Measures based Selective Ensemble (PDMSEN) is proposed in this paper. Furthermore, an improved method is studied to advance the speed of the algorithm and support parallel computing. Finally, through applying BP neural networks as base learners, the experiment is carried out on selected UCI database and the improved algorithm is compared with Bagging and GASEN (Genetic Algorithm based Selected Ensemble) algorithms. Experimental results demonstrate that the learning speed of the proposed algorithm is superior to that of the GASEN algorithm with the same learning performance.