Date of Award
Fall 1-6-2023
Document Type
Thesis
Degree Name
Master of Arts (MA)
Department
Computer Science
First Advisor
Professor Anita Raja
Second Advisor
Professor Ioannis Stamos
Third Advisor
Professor Ansaf Salleb-Aouissi
Academic Program Adviser
Professor Subash Shankar
Abstract
An unsupervised learning pipeline for discrete Bayesian networks is proposed to facilitate prediction, decision making, discovery of patterns, and transparency in challenging real-world AI applications, and contend with data limitations. We explore methods for discretizing data, and notably apply the pipeline to prediction and prevention of preterm birth.
Recommended Citation
Mallia, Daniel, "Towards an Unsupervised Bayesian Network Pipeline for Explainable Prediction, Decision Making and Discovery" (2023). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/978