Data synthesis method preserving correlation of features

Abstract

Abundant data are essential for improving the performance of machine learning algorithms. Thus, if only limited data are available, data synthesis can be used to enlarge datasets. Data synthesis methods based on the covariance matrix are useful because of their fast data synthesis capabilities. However, artificial datasets generated via classical techniques show statistical discrepancies when compared to original datasets. To address this problem, we developed a new data synthesis method that preserves the correlation (between features) observed in the original dataset. This preservation was realized by considering not only the correlation but also the random noises used in data synthesis process. This method was applied to various biosignals (i.e., electrocortiography, electromyogram, and electrocardiogram), wherein data points are insufficient. Several classifiers (i.e., convolutional neural network, support vector machine, and k-nearest neighbor) were used to verify that the classification accuracy can be improved by the proposed data synthesis method.