Islamic Azad University, South Tehran Branch, Tehran, Iran.
PLoS One. 2011;6(10):e24386. doi: 10.1371/journal.pone.0024386. Epub 2011 Oct 26.
This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization.
本文展示了一种基于堆叠式综合方法的组合神经网络模型在心电图（ECG）节拍分类中的应用。在传统的堆叠式综合方法中，组合器学习将基分类器的输出映射到目标数据上。我们声称向基分类器的输出添加输入模式有助于组合器获取有关输入空间的知识，并且因此在相同任务上表现更好。实验结果支持我们的主张，即根据输入空间增加的知识，提高了所提出的方法的性能，该方法称为改进的堆叠式综合。特别是，对于在训练阶段之前从未见过的 14966 个 ECG 节拍的分类，与包括 Max、Min、Average、Product、Majority Voting、Borda Count、Decision Templates、基于粒子群优化的加权平均和堆叠式综合在内的十种流行的分类器融合方法中的最佳方法相比，改进的堆叠式综合方法将错误率降低了 12.41%。
