A Feature Selection-based Ensemble Method for Arrhythmia Classification
A Feature Selection-based Ensemble Method for Arrhythmia Classification
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DOI：
10.3745/jips.2013.9.1.031
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发表时间：
2013-03-01
影响因子：
1.6
通讯作者：
Ryu, Keun Ho
中科院分区：
文献类型：
--
作者：
Namsrai, Erdenetuya;Munkhdalai, Tsendsuren;Ryu, Keun Ho
在本文中，提出了一种使用特征选择模式构建分类器集合的新方法。特征选择方案识别影响心律失常分类的最佳特征集。首先，通过将特征选择模式应用于原始数据集来提取多个特征子集。然后利用每个特征子集构建分类模型。最后，我们通过采用投票方法组合分类模型以形成分类集成。我们方法中的投票方法涉及分类错误率和特征选择率来计算集成中每个分类器的分数。在我们的方法中，特征选择率取决于特征子集的提取顺序。在实验中，我们将我们的方法应用于心律失常数据集并生成了三个最不相交的特征集。然后，我们基于前三个特征子集构建了三个分类器，并使用投票方法形成分类器集合。我们的方法可以提高高维数据集的分类精度。每个分类器的性能及其集成的性能都高于基于数据集整个特征空间的分类器的性能。该方法提高了分类性能，并且可以构建更稳定的分类模型。
In this paper, a novel method is proposed to build an ensemble of classifiers by using a feature selection schema. The feature selection schema identifies the best feature sets that affect the arrhythmia classification. Firstly, a number of feature subsets are extracted by applying the feature selection schema to the original dataset. Then classification models are built by using the each feature subset. Finally, we combine the classification models by adopting a voting approach to form a classification ensemble. The voting approach in our method involves both classification error rate and feature selection rate to calculate the score of the each classifier in the ensemble. In our method, the feature selection rate depends on the extracting order of the feature subsets. In the experiment, we applied our method to arrhythmia dataset and generated three top disjointed feature sets. We then built three classifiers based on the top-three feature subsets and formed the classifier ensemble by using the voting approach. Our method can improve the classification accuracy in high dimensional dataset. The performance of each classifier and the performance of their ensemble were higher than the performance of the classifier that was based on whole feature space of the dataset. The classification performance was improved and a more stable classification model could be constructed with the proposed approach.