基于近似约简与最优采样的集成剪枝

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 Pruning Based on Approximate Reducts and Optimal Sampling

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.