Application of convolutional neural network to traditional data

作者：

Highlights：

• Propose a feature grid-based CNN model, FGCN, on traditional data.

• Propose methods of converting instance with form of 1-d vector to feature grid.

• The performance of FGCN model has reached the state-of-the-art technique XGBoost.

• The positions of features in the grid have little influence on prediction accuracy.

• Fully connected layers in CNN give little marginal classification performance.

摘要

•Propose a feature grid-based CNN model, FGCN, on traditional data.•Propose methods of converting instance with form of 1-d vector to feature grid.•The performance of FGCN model has reached the state-of-the-art technique XGBoost.•The positions of features in the grid have little influence on prediction accuracy.•Fully connected layers in CNN give little marginal classification performance.

论文关键词：Convolutional neural network,Traditional data,Data conversion

论文评审过程：Received 22 February 2020, Revised 29 August 2020, Accepted 28 October 2020, Available online 3 November 2020, Version of Record 24 January 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.114185