One-dimensional VGGNet for high-dimensional data

Abstract

We consider a deep learning model for classifying high-dimensional data and seek to achieve optimal evaluation accuracy and robustness based on multicriteria decision-making (MCDM) for high-dimensional data analysis applications during comprehensive evaluation (CE) activities. We propose a novel one-dimensional visual geometry group network (1D_VGGNet) to overcome the problem that high-dimensional data are too complicated and unstable to be feasibly applied. Then, to effectively handle one-dimensional MCDM, we present a 1D_VGGNet classifier to replace the two-dimensional convolution operation applied to image data with a one-dimensional convolution operation applied to one-dimensional MCDM. Furthermore, to solve the invariance problem of the generated feature maps, the maxpooling kernel size can be flexibly adjusted to effectively meet the requirements of reducing the feature map dimension and speeding up training and prediction on different datasets. The improvement is reasonable for various high-dimensional data application scenarios. Moreover, we propose a novel objective function to accurately evaluate network performance since the objective function includes a variety of representative performance evaluation metrics, and the average value is calculated as one of the CE metrics. The experimental results illustrate that the proposed framework outperforms a one-dimensional convolutional neural network (1D_CNN) for comprehensive classification on the Shaoxing University student achievement dataset and the MIT-BIH Arrhythmia database and achieves average gains of 36.3% and 12.1% in terms of the designated evaluation metric.