基于博弈论准确性和差异性兼优的选择性集成建模方法及其应用

Selective Ensemble Modeling Method Based on Game Theory Considering Accuracy and Diversity Simultaneously and Its Application

集成学习相较于单模型具有更好的预测精度和泛化能力,被广泛应用于工业过程的质量预测.基学习器之间的多样性和基学习器的准确性对集成的泛化能力影响极大.为了进一步提高集成模型的泛化能力,提出一种同时考虑准确性和差异性的选择性集成建模方法.以在线极限学习机作为基学习器,将基学习器的准确性和基学习器对集成模型多样性的贡献率作为博弈双方,利用博弈论原理求解得出使集成模型准确性和多样性都达到最优的选择方案,使集成模型的准确性和多样性兼优;模型预测完成后,综合当前误差和历史记录误差对基学习器的权重进行在线更新,实现在线测量阶段对建模对象特性的动态自适应.最后,使用公开数据集和实际工业数据验证了所提算法的合理性和有效性.

Compared with single model,ensemble learning has better prediction accuracy and generalization ability,which is widely used in industrial process quality prediction.The diversity between basic learners and the accuracy of basic learners have a great impact on the generalization ability of the ensemble model.To improve the generalization ability of ensemble model,a selective ensemble modeling method based on online extreme learning machine and game theory was proposed in this paper.Online sequential extreme learning machine(OSELM)was used as base learner,and the accuracy of the base learner and the contribution rate of the base learner to the diversity of the ensemble model were regarded as the two sides of the game.Game theory was used to deal with the optimal selection scheme for the accuracy and diversity of the ensemble model,and the accuracy and diversity of the ensemble model were optimized.After the model prediction was completed,the weights of the base learner were updated online by integrating current errors and historical errors,therefore,the ensemble model was dynamically adaptive.The rationality and effectiveness of the proposed algorithm were verified by using open data sets and real industrial data.