fMRI解码中分类与降维方法的比较

N. Alamdari, E. Fatemizadeh
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引用次数: 3

摘要

在过去的几年里,人们对使用功能性磁共振成像(fMRI)来绘制大脑图谱越来越感兴趣。为了解码fMRI数据中的脑模式,我们需要可靠和准确的分类器。为了实现这个目标,我们比较了11种流行的模式识别方法的性能。在进行模式识别之前,应用降维方法可以提高分类性能;因此,对感兴趣区域(RDI)的七种方法进行了比较,以回答以下问题:哪种降维方法性能最好?在这两项任务中,除了测量预测精度外,我们还估计了精度的标准差,以实现更可靠的方法。根据所有结果,我们建议使用线性核支持向量机(C-SVM和v-SVM),或在Active或maxDis特征选择方法制备的低维子集上使用随机森林分类器对大脑活动模式进行更有效的分类。
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Comparison of classification and dimensionality reduction methods used in fMRI decoding
In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve the classification performance; therefore, seven methods in region of interest (RDI) have been compared to answer the following question: which dimensionality reduction procedure performs best? In both tasks, in addition to measuring prediction accuracy, we estimated standard deviation of accuracies to realize more reliable methods. According to all results, we suggest using support vector machines with linear kernel (C-SVM and v-SVM), or random forest classifier on low dimensional subsets, which is prepared by Active or maxDis feature selection method to classify brain activity patterns more efficiently.
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