Abstract
One of the growing trends in machine learning is the use of data generation techniques, since the performance of machine learning models is dependent on the quantity of the training dataset. However, in many medical applications, collecting large datasets is challenging due to resource constraints, which leads to overfitting and poor generalization. This paper introduces a novel method, Artificial Data Point Generation in Clustered Latent Space (AGCL), designed to enhance classification performance on small medical datasets through synthetic data generation. The AGCL framework involves feature extraction, K-means clustering, cluster evaluation based on a class separation metric, and the generation of synthetic data points from clusters with distinct class representations. This method was applied to Parkinson's disease screening, utilizing facial expression data, and evaluated across multiple machine learning classifiers. Experimental results demonstrate that AGCL significantly improves classification accuracy compared to baseline, GN and kNNMTD. AGCL achieved the highest overall test accuracy of 83.33% and cross-validation accuracy of 90.90% in majority voting over different emotions, confirming its effectiveness in augmenting small datasets.
Abstract (translated)
机器学习的一个不断增长的趋势是使用数据生成技术，因为机器学习模型的性能取决于训练数据集的数量。然而，在许多医疗应用中，收集大量数据是非常具有挑战性的，由于资源限制，导致过拟合和欠拟合。本文介绍了一种名为人工数据点生成在聚类潜在空间（AGCL）的新方法，旨在通过合成数据生成来提高小型医疗数据的分类性能。AGCL框架包括特征提取、K-means聚类、基于类分离指标的聚类评估和从聚类中生成合成数据点。将该方法应用于帕金森病筛查，利用面部表情数据，并在多个机器学习分类器上进行评估。实验结果表明，AGCL相对于基线、GN和kNNMTD显著提高了分类准确率。AGCL在不同的情感上的整体测试准确率为83.33%，交叉验证准确率为90.90%，证实了其在增强小数据集的有效性。
URL
https://arxiv.org/abs/2409.17685
