文献类型：期刊文献

英文题名：One-dimensional VGGNet for high-dimensional data

作者：Feng, Sheng[1];Zhao, Liping[1];Shi, Haiyan[1];Wang, Mengfei[2];Shen, Shigen[3];Wang, Weixing[2]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]Changan Univ, Sch Informat Engn, Xian 710064, Peoples R China;[3]Huzhou Univ, Sch Informat Engn, Huzhou 313000, Peoples R China

年份：2023

卷号：135

外文期刊名：APPLIED SOFT COMPUTING

收录：SCI-EXPANDED(收录号：WOS:000967879400001)、、EI(收录号：20230413440434)、Scopus(收录号：2-s2.0-85146692959)、WOS

基金：This work is supported in part by the National Science Foundation of China No. 62271321 and 61871289, Zhejiang Provincial Natural Science Foundation of China under Grants LZ22F020002, LTY22F020003, LGG22F010004, and LY19F020014, Ministry of Education Industry University Cooperation Collaborative Education Project under Grant 202101011025, and Humanities and Social Sciences Project of Shaoxing University under Grant 2021LJ001. The views expressed are solely those of the authors.

语种：英文

外文关键词：High-dimensional data; Deep learning classification; One-dimensional visual geometry group; network (1D_VGGNet); One-dimensional convolution; Comprehensive evaluation (CE)

外文摘要：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 classifica-tion 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.(c) 2023 Elsevier B.V. All rights reserved.