Physical Sciences and Mathematics Commons^™

Machine Learning Methods For Computational Phenotyping Using Patient Healthcare Data With Noisy Labels, Praveen Kumar

Computer Science ETDs

Positive and Unlabeled (PU) learning problems abound in many real-world applications. In healthcare informatics, diagnosed patients are considered labeled positive for a specific disease, but being undiagnosed does not mean they can be labeled negative. PU learning can improve classification performance, and estimate the positive fraction, α, among unlabeled samples. However, algorithms based on the Selected Completely At Random (SCAR) assumption are inadequate when the SCAR assumption fails (e.g., severe cases overrepresented), and when class imbalance is substantial. This dissertation presents and evaluates new algorithms to overcome these limitations. The proposed methods outperform the state-of-art for α-estimation, enhance classification performance, …

Data Mining Of Chinese Social Networks: Factors That Indicate Post Deletion, Meisam Navaki Arefi

Computer Science ETDs

Widespread Chinese social media applications such as Sina Weibo (Chinese Twitter), the most popular social network in China, are widely known for monitoring and deleting posts to conform to Chinese government requirements. Censorship of Chinese social media is a complex process that involves many factors. There are multiple stakeholders and many different interests: economic, political, legal, personal, etc., which means that there is not a single strategy dictated by a single government authority. Moreover, sometimes Chinese social media do not follow the directives of government, out of concern that they are more strictly censoring than their competitors.

One crucial question …

Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm

Computer Science ETDs

Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …