Application of a New VAE-MF Generative Model in TCD Dataset

Xueying Zhang, Xiaoyu Chen, Yuling Guo, Suzhe Wang, Wenhui Jia
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Abstract

Transcranial Doppler (TCD) is a non-invasive method for detecting ischemic stroke disease and is widely used in clinical diagnosis. Due to the obvious imbalance of medical data, oversampling is needed to balance it. Although the oversampling method is one of the important means to solve the problem of imbalanced data classification, the traditional oversampling method will inevitably introduce noise, which will affect the classification results. To address this issue, we proposed a new oversampling method combining the membership function (MF) and the variational autoencoder (VAE). Our method utilized VAE as a generative model to generate new samples to reduce the Imbalance Ratio (IR) of the dataset. Then the new samples are filtered using MF to weaken the impact of the noise introduced by the oversampling method on classification performance. In addition, in view of the weak adaptability of traditional MFs to complex sample distributions in practical applications, a new MF is proposed by using kernel function mapping and hypersphere. The classification experiments on TCD imbalanced dataset prove that the oversampling method we proposed has improved classification performance on multiple evaluation criteria compared with traditional oversampling methods.
一种新的VAE-MF生成模型在TCD数据集中的应用
经颅多普勒(Transcranial Doppler, TCD)是一种检测缺血性脑卒中疾病的无创方法,在临床诊断中得到广泛应用。由于医疗数据的明显不平衡,需要进行过采样来平衡。虽然过采样方法是解决数据分类不平衡问题的重要手段之一,但传统的过采样方法不可避免地会引入噪声,影响分类结果。为了解决这一问题,我们提出了一种结合隶属度函数(MF)和变分自编码器(VAE)的过采样方法。我们的方法利用VAE作为生成模型来生成新的样本,以降低数据集的不平衡比(IR)。然后对新样本进行MF滤波,以减弱过采样方法引入的噪声对分类性能的影响。此外,针对传统MF在实际应用中对复杂样本分布的适应性较弱的问题,提出了一种利用核函数映射和超球的新MF。在TCD不平衡数据集上的分类实验证明,与传统的过采样方法相比,我们提出的过采样方法在多个评价标准上的分类性能有所提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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