Doctoral Dissertation Research: Bringing the Power of Deep Learning to Large-Scale Ordinal Data Classification

Kennesaw State University Research And Service Foundation, Kennesaw GA

Investigators

Abstract

This doctoral dissertation research project will develop a more effective and efficient classification method for ordinal data. Due to the explosion in the use of digital data, there has been a dramatic increase in the number of ordinal datasets that have hundreds of thousands or even millions of records. Examples include ratings surveys found on sites like Amazon and Yelp, large corporation customer satisfaction/net promoter surveys, and the aggregation of medical history records. Current classification methods are inadequate for analyzing large ordinal datasets. The investigators will develop a classification method that facilitates the analysis of large ordinal datasets across a spectrum of application domains. Open-source software will be developed and made publicly available. Learning material and case studies will be made available for educators to adopt in relevant courses and experiential learning opportunities. As a Doctoral Dissertation Research Improvement award, support is provided to enable a promising student to establish a strong, independent research career. This doctoral dissertation research will develop a highly scalable ordinal classification method that can be applied to both structured and unstructured (e.g., images and text) ordinal data. A core component of the method is a loss function that called Ordinal Hyperplane Loss (OHPL). OHPL is particularly designed for data with ordinal classes and enables deep learning techniques to be applied to the ordinal classification problems. By minimizing OHPL, a deep neural network learns to map data to an optimal space where the distance between points and their class centroid hyper-plane are minimized while a nontrivial ordinal relationship among classes are maintained. Preliminary experimental results indicate that deep neural network with OHPL optimizing significantly outperforms the state-of-the-art alternatives on classification accuracies across multiple datasets. This research will examine strategies that scale the OHPL based learning to big ordinal data. The investigators will apply the OHPL-based learning to real-life critical applications such as determining the severity/stages of a disease. They will develop a ready-to-use open-source package on the OHPL deep learning strategy and make it publicly available. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.