Turner Business Faculty Trailblazers

Molding Machine Learning for Business

Among machine learning techniques, classification techniques are useful for various business applications, but classification algorithms perform poorly with imbalanced data.  In their study appearing in a 2022 issue of Information Systems Frontiers, Turner College assistant professor of management information systems Yoon Lee and Chris Bang of Auburn University – Montgomery propose a classification technique with improved binary classification performance on both the minority and majority classes of imbalanced structured data.  Their technique involves three steps, including creation of a balanced training set via under-sampling and converting it into an image depicting a line graph.  The third and final step in the process includes training a Convolutional Neural Network, which is a Deep Learning algorithm that can absorb an input image, assign importance to various objects in the image, and differentiate one from the other, using the images.  As Lee explained to Turner Business, “For the experiments, we applied the proposed framework to six datasets from the University of California – Irvine Repository.  The proposed model achieved the best receiver operating characteristic curve on all six datasets, and Balanced Accuracy on five of the six datasets.”  Thus, Lee’s experimentation demonstrates that the combination of under-sampling and Convolutional Neural Networks is a viable approach for imbalanced structure data classification.