Improving 1d Convolutional Neural Network (1d Cnn) Performance in Processing Tabular Datasets Using Principal Component Analysis

Abstract Convolutional Neural Network (CNN) has been used significantly in various applications to process data that have spatial or temporal relationship, such as image and video recognition, medical image analysis, natural language processing, and speech recognition. This study used CNN to process tabular datasets with unknown spatial and temporal relationship between attributes. It used a principal component analysis (PCA) process to obtain the relationship between the attributes, which are connected based on their variance values and expected to provide a spatial relationship. Therefore, after being transformed with PCA, the dataset remains in the tabular form but between attributes already have a relationship. Because the transformed dataset is still a one-dimensional dataset, the CNN model used is 1D CNN which is more suitable for processing one-dimensional data. To compare the performance of 1D CNN against the original dataset and the dataset that has been transformed with PCA, the Iris and Covertype datasets were used. By using the Wilcoxon difference test, it turns out that two datasets that have been transformed with PCA provide better performance than using the original dataset. Furthermore, the PCA implementation to the NCI-60 dataset using 1D CNN performed better compared to previous research. This research has an accuracy of 83.34%. The previous research transformed the dataset into a two-dimensional image classified using 2D CNN at an accuracy of 75.4%.