基于准确性和多样性的在线动态选择集成建模方法

Ensemble Modeling Method of Online Dynamic Selection Based on Accuracy and Diversity

为了解决复杂工业过程中的概念漂移问题,提高集成学习模型的泛化性能,在保证集成学习模型精度的基础上,提出了一种用于优化多样性的基学习器在线动态选择集成建模方法.该方法以在线极限学习机作为基学习器,按照基学习器在滑动窗口上的分类精度对其进行逆序排序,将基学习器在滑动窗口上的其他性能指标作为特征属性,依次利用近似线性依靠条件挑选出准确且多样的基学习器用于集成输出,提高了集成学习模型在处理概念漂移数据流时的分类精度.最后,使用合成数据集和公开数据集验证了所提算法的合理性与有效性.

To solve the problem of concept drift in complex industrial process and to improve the generalization performance of ensemble learning model,an ensemble modeling method of online dynamic selection for optimizing the diversity of the base learners was proposed,on the basis of ensuring the accuracy of the ensemble learning model.Online sequential extreme learning machine was used as the base learner,and the base learners were sorted in reverse order according to their classification accuracy on the sliding window.The other performance indexes of the basic learners on the sliding window were used as the feature attributes,and the approximate linear dependence condition was used to select accurate and diverse base learners for ensemble output,which improves the classification accuracy of the ensemble algorithm in dealing with the concept drift data stream.Finally,the rationality and effectiveness of the proposed algorithm were verified by using the synthetic data sets and real-world data sets.