基于近似约简与最优采样的集成剪枝

Ensemble Pruning Based on Approximate Reducts and Optimal Sampling

集成学习被广泛用于提高分类精度,近年来的研究表明,通过多模态扰乱策略来构建集成分类器可以进一步提高分类性能.本文提出了一种基于近似约简与最优采样的集成剪枝算法(EPA_AO).在EPA_AO中,我们设计了一种多模态扰乱策略来构建不同的个体分类器.该扰乱策略可以同时扰乱属性空间和训练集,从而增加了个体分类器的多样性.我们利用证据KNN(K-近邻)算法来训练个体分类器,并在多个UCI数据集上比较了EPA_AO与现有同类型算法的性能.实验结果表明,EPA_AO是一种有效的集成学习方法.

Ensemble learning has been widely used for improving classification accuracy.Recent studies show that building ensemble classifiers through a multi-modal perturbation strategy can further improve classification performance.In this study,we propose an ensemble pruning algorithm based on approximate reducts and optimal sampling(EPA_AO).In EPA_AO,we design the multi-modal perturbation strategy to build different individual classifiers.The proposed perturbation strategy can simultaneously perturb the attribute space and training set,which can improve the diversity of individual classifiers.We use the evidential K-nearest neighbor(KNN)algorithm to train individual classifiers and compare EPA_AO with existing algorithms of the same type on multiple UCI data sets.Experimental results show that EPA_AO is an effective ensemble learning approach.