Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis
Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis
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DOI：
10.1016/j.compbiomed.2011.03.001
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发表时间：
2011-05-01
影响因子：
7.7
通讯作者：
Ozcift, Akin
中科院分区：
文献类型：
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作者：
Ozcift, Akin
监督分类算法是计算机辅助诊断系统设计中常用的一种算法。在这项研究中，我们提出了一个基于恢复策略的随机森林（RF）集成分类器，以提高心律失常的诊断。随机森林是一种集成分类器，它由许多决策树组成，输出的类是各个树输出的类的模式。以这种方式，从分类性能的角度来看，RF集成分类器比单个树表现得更好。一般来说，样本量分布不平衡的多类数据集很难进行类区分分析。心律失常是这样一个数据集，它具有多个类别和小样本量，因此足以测试我们基于呼吸的训练策略。该数据集包含14种心律失常类型的452个样本，其中11个类别的样本量小于15。我们的诊断策略包括两部分：（i）基于相关性的特征选择算法用于从心律失常数据集中选择相关特征。(ii)RF机器学习算法被用来评估所选择的特征的性能与简单的随机采样，以评估所提出的训练策略的效率。分类器的准确率为90.0%，这是一个相当高的诊断性能的心律失常。此外，还进行了三个案例研究，即，甲状腺，心脏分娩描记术和听力学，被用来衡量所提出的方法的有效性。实验结果证明了随机抽样策略在训练RF集成分类算法中的有效性。(C)2011爱思唯尔有限公司版权所有。
Supervised classification algorithms are commonly used in the designing of computer-aided diagnosis systems. In this study, we present a resampling strategy based Random Forests (RF) ensemble classifier to improve diagnosis of cardiac arrhythmia. Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. In this way, an RF ensemble classifier performs better than a single tree from classification performance point of view. In general, multiclass datasets having unbalanced distribution of sample sizes are difficult to analyze in terms of class discrimination. Cardiac arrhythmia is such a dataset that has multiple classes with small sample sizes and it is therefore adequate to test our resampling based training strategy. The dataset contains 452 samples in fourteen types of arrhythmias and eleven of these classes have sample sizes less than 15. Our diagnosis strategy consists of two parts: (i) a correlation based feature selection algorithm is used to select relevant features from cardiac arrhythmia dataset. (ii) RF machine learning algorithm is used to evaluate the performance of selected features with and without simple random sampling to evaluate the efficiency of proposed training strategy. The resultant accuracy of the classifier is found to be 90.0% and this is a quite high diagnosis performance for cardiac arrhythmia. Furthermore, three case studies, i.e., thyroid, cardiotocography and audiology, are used to benchmark the effectiveness of the proposed method. The results of experiments demonstrated the efficiency of random sampling strategy in training RF ensemble classification algorithm. (C) 2011 Elsevier Ltd. All rights reserved.