一种基于Bagging和混淆矩阵的自适应选择性集成

摘要

为了平衡集成学习中差异性和准确性的关系并提高学习系统的泛化性能，提出一种基于 Bagging 和混淆矩阵的选择性集成方法。基本思想是通过扰动训练集和特征空间生成基分类器，根据每一个基分类器的混淆矩阵构造一个基分类器间相关性的度量矩阵；然后基于相关性度量矩阵对基分类器集合进行子集划分，在每个划分中选择一个基分类器参与集成；最后用多数投票法融合所选基分类器的决策结果，并通过仿真实验验证该方法的有效性。%To balance the diversity and the accuracy in ensemble learning and improve the generalization performance of learning system ,a selective ensemble algorithm based on Bagging and confusion matrix is proposed .By disturbing the training set and feature space ,base classifiers are generated ,of which the confusion matrix is used to construct a measure matrix of the diversity between classifiers ;then based on the measure matrix ,base classifier set is divided into different subsets ,from which each base clas-sifier is selected for ensemble ;finally ,majority voting method is utilized to fusion the base classifiers’ recognition results and experi-ments have been done to attest the validity of the proposed algorithm .