Non-image Data Classification with Convolutional Neural Networks

Convolutional Neural Networks (CNNs) is one of the most popular algorithms for deep learning which is mostly used for image classification, natural language processing, and time series forecasting. Its ability to extract and recognize the fine features has led to the state-of-the-art performance. CNN has been designed to work on a set of 2-D matrices whose elements show some correlation with neighboring elements such as in image data. Conversely, the data examples represented as a set of 1-D vectors -- apart from time series data -- cannot be used with CNN, but with other Artificial Neural Networks (ANNs). We have proposed some novel preprocessing methods of data wrangling that transform a 1-D data vector to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN. To our knowledge this work is novel on non-image to image data transformation for non-time series data. The transformed data processed with CNN using VGGnet-16 shows a competitive result in classification accuracy compared to canonical ANN approach with high potential for further improvements.